Innovative mycosynthesis of chitosan nanoparticles via bionanofactory Penicillium crustosum: optimization, characterization, and application against building-deteriorating fungus, Trichosporon asahii
Hadeel El-Shall, Noura El-Ahmady El-Naggar, Shaimaa Elyamny, Asmaa A. El-Sawah, Marwa Eltarahony
TL;DR
This paper explores using chitosan nanoparticles made by a fungus to fight building-deteriorating fungi, offering an eco-friendly solution to biocorrosion.
Contribution
The study introduces a novel eco-friendly method for synthesizing chitosan nanoparticles using Penicillium crustosum for anti-fungal applications.
Findings
Chitosan nanoparticles (CNPs) were successfully biosynthesized with an average size of 26.19 nm.
Higher concentrations of CNPs (250 and 500 µg/mL) showed significant fungicidal activity against Trichosporon asahii.
CNPs caused structural damage to fungal spores and biofilm disruption, indicating potential for bio-safe coatings.
Abstract
Biocorrosion of building materials is a global challenge that threatens both modern buildings and cultural heritage. This degradation is largely driven by interactions between microbes, particularly fungi, and various gaseous effluents containing nitrogenous, sulfurous, and carbonaceous aerosols from anthropogenic activities, leading to progressive biocorrosion of construction surfaces. Therefore, this study aims to reduce such detrimental effects using chitosan nanoparticles (CNPs). An eco-friendly biosynthesis approach was employed, using the cell-free supernatant of Penicillium sp. strain HNSAM-7 to produce the CNPs. This strain has been identified as Penicillium crustosum strain HNSAM-7 according to the analysis of ITS region sequence, together with its morphological characteristics. The physicochemical properties of CNPs were characterized; SEM and TEM analyses revealed that the…
Genes, proteins, chemicals, diseases, species, mutations and cell lines named across the full text — each resolved to its canonical identifier and authoritative record.
Click any figure to enlarge with its caption.
Figure 10
Figure 11
Figure 12
Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8
Figure 9- —City of Scientific Research and Technological Applications (SRTA City)
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsMicrobial Applications in Construction Materials · Building materials and conservation · Nanoparticles: synthesis and applications
Introduction
Nanomaterials have attracted significant attention due to their unique physicochemical properties and wide range of potential applications. Nanoparticles impart distinctive characteristics and behaviors that are not observed in bulk materials of the same composition. Owing to their high surface area-to-volume ratio, they exhibit enhanced physical, chemical, and biological activities, making them highly valuable in various applications [1, 2].
Chitosan is an amino polysaccharide derived from the deacetylation of chitin and has been widely used in the fabrication of chitosan nanoparticles (CNPs). CNPs exhibit exceptional physicochemical and biological characteristics, including nontoxicity, biocompatibility, biodegradability, and environmental safety [3, 4]. The conversion of chitosan into its nanostructured form significantly expands its potential applications in a wide range of applications such as agriculture, textiles, antimicrobial, and water treatment. Moreover, CNPs are considered safe for applications in the pharmaceutical and medical fields, including dentistry, ophthalmology, antioxidant, skincare, hair care, drug delivery, bioimaging, diagnostics, and cancer therapy [5–9]. Due to their small particle size and positive surface charge, CNPs have been effectively used as fillers in pectin-based edible films for food packaging to enhance both mechanical strength and barrier performance [10]. Furthermore, CNPs exhibit strong antimicrobial activity against the most common and destructive phytopathogenic fungus, Botrytis cinerea [11], Fusarium culmorum [12], as well as the bacterium, Pectobacterium carotovorum, the causal agent of potato soft rot [13].
Several approaches have been used for the preparation of chitosan nanoparticles, including emulsification and crosslinking, which was the first method described in the literature. However, this method is no longer favored, as glutaraldehyde, commonly used as a crosslinking agent, was found to cause high toxicity and compromise drug integrity [14]. Precipitation-based techniques based on emulsification and precipitation have also been utilized to synthesize chitosan nanoparticles; however, these methods typically yield particles larger than 600–800 nm [5]. Other reported methods include ionic gelation [15], ionic gelation coupled with radical polymerization [16], self-assembly [17], spray drying [18], supercritical CO_2_-assisted solubilization and atomization [19]. Despite their effectiveness, these chemical and physical methods present several limitations, such as long processing times, large particle sizes, the use of hazardous chemicals, the need for high temperatures or high pressure. Additionally, large-scale production remains difficult to achieve, and the nanoparticles produced by these methods often possess restricted applications [5]. Consequently, there is a growing demand for green synthesis methods capable of producing ultrafine CNPs, particularly for pharmaceutical and biomedical applications [20, 21]. Green synthesis approaches that utilize natural sources such as fungi, yeast, plants, and bacteria offer promising alternatives to conventional chemical methods. These eco-friendly techniques help mitigate the drawbacks associated with traditional synthesis, such as toxicity, high energy demand, and harsh processing conditions [22]. Recent findings by El-Basiouny et al. [23] further support the biosafety of such nanomaterials, demonstrating that bio-mediated synthesis can effectively mitigate the potential cytotoxic effects often associated with chemically synthesized nanoparticles. They demonstrate that CNPs, when incorporated into alginate-based nanocomposite films synthesized using Aspergillus flavus, exhibited excellent biocompatibility and negligible cytotoxicity toward plant and microbial cells during postharvest tomato preservation. Such findings reinforce the biosafe nature of green-synthesized CNPs and support their broader utilization across biomedical, environmental, and industrial applications without posing adverse effects on living systems.
Recently, construction materials such as cement, plaster, marble, stone, primers, and paints have been found to undergo natural aging accompanied by progressive deterioration. This deterioration is influenced by physicochemical factors including rainfall, temperature fluctuations, wind, humidity, sunlight, and frost, as well as intrinsic petrographic properties such as grain size, texture, porosity, permeability, and mineral composition [24]. Biocorrosion has been documented in numerous modern and historical structures, including dams, bridges, hospitals, and heritage sites such as the Giza pyramid and Seti I Tomb in Egypt and La Palma in Spain [24]. Anthropogenic pollutants from fossil fuel combustion, industrial and domestic wastes, vehicle emissions, and agricultural runoff further aggravate this deterioration. Nitrogenous, sulfurous, and carbonaceous aerosols combine with atmospheric moisture and deposit on building surfaces, creating favorable conditions for microbial colonization. Microorganisms can colonize surfaces (epilithic), infiltrate cracks (chasmolithic), or penetrate deep within materials (endolithic), accelerating biodeterioration [25]. Colonization typically begins with phototrophic organisms such as algae, cyanobacteria, and lichens that utilize CO_2_ and stone minerals for growth, paving the way for heterotrophic bacteria and fungi. These organisms secrete enzymes, pigments, and biogenic inorganic and organic acids that react with stone constituents, resulting in chemical and physical damage [24]. Consequently, these biochemical processes lead to discolouration, mineral destabilization, physical cracking, decaying paint layers, and overall disfigurement of building materials [26]. Fungi, particularly saprophytic and meristematic species, play a major role due to their hyphal penetration, which increases porosity and causes black crusts, especially in tropical and Mediterranean regions [27]. Generally, saprophytic basidiomycetes such as Trichosporon asahii can colonize a vast array of ecological niches like mural paintings; nonetheless, ascomycetes are the main biodeteriogens with different genera such as Aspergillus, Alternaria, Penicillium, Exophiala, Trimmatostroma, Sarcinomyces, and Chaetomium [28, 29]. Moreover, fungal bioaerosols release volatile metabolites, pigments, and mycotoxins that threaten human health, causing allergies, superficial infections, mycotoxicosis, and systemic mycoses, particularly in immunosuppressed individuals [30, 31]. To address these challenges, regulations now promote the use of eco-labeled hygienic paints to minimize bioreceptivity, avoid biodeterioration, and mitigate health risks. A promising modern strategy combines polymeric and nanotechnological approaches [32]. In particular, CNPs have emerged as effective antimicrobial agents owing to their biopolymeric, biodegradable nature and nanoscale properties. Their colorless nature also makes them suitable for coating applications in the construction industry, meeting esthetic requirements, which were not provided by numerous inorganic biocides [33].
To the best of our knowledge, this is the first report that aims to explore the green synthesis of CNPs using the cell-free supernatant of Penicillium crustosum strain HNSAM-7 as a natural crosslinking and stabilizing agent, and optimizes their production using FCCCD (Face-centered central composite design). The biosynthesized CNPs were thoroughly characterized to assess their physicochemical properties, and their antifungal efficacy was evaluated in vitro against Trichosporon asahii through a killing-time assay, supported by dynamic analyses of concentration- and time-dependent effects, as well as microstructural observations to validate their potential as possible biosafe hygienic coating materials against fungal-induced biocorrosion of building materials for protecting construction and heritage materials. The integration of Penicillium-derived metabolites as natural crosslinking and stabilizing agents offers a sustainable and non-toxic alternative to conventional chemical methods.The produced CNPs exhibit uniform, small particle size, high crystallinity, which enhances stability, adsorption capacity, and potential antifungal activity, as confirmed through SEM, TEM, and XRD analyses. Moreover, this work represents the first demonstration of using such biosynthesized CNPs as possible protective coatings to mitigate Trichosporon asahii-associated biodeterioration, highlighting their dual advantages of environmental safety and structural protection. Overall, this study integrates principles of microbiology, nanotechnology, and conservation science to introduce an innovative, eco-friendly nanotechnological strategy aimed to prevent the microbial-induced corrosion in both modern infrastructure and cultural heritage materials.
Materials and methods
Microorganism and culture maintenance
Penicillium sp. strain HNSAM-7 used in this study was kindly provided by the Environmental Biotechnology Department, GEBRI, SRTA-City, New Borg El Arab City, Alexandria, Egypt. The strain of Trichosporon asahii was purchased from Moubasher Mycology Center, Assiut University (AUMMC), Egypt. The fungal strains were cultured on Petri plates containing Czapek–Dox medium that was composed of g/L: sucrose, 30; sodium nitrate, 3; potassium chloride, 0.5; magnesium sulfate heptahydrate, 0.5; di-potassium hydrogen phosphate, 1; iron (II) sulfate heptahydrate, 0.01; and agar, 15. The pH was adjusted to 7. After the incubation at 30 ºC for 5 days, the plates were maintained at 4 ºC for further use.
Scanning electron microscopy for Penicillium sp. strain HNSAM-7
For scanning electron microscopy (SEM), the gold-coated dehydrated specimen was examined at different magnifications with Analytical Scanning Electron Microscope (Jeol JSM-6360 LA operating at 20 kV) at Electron Microscope unit, the Central Laboratory, City of Scientific Research and Technological Applications, Alexandria, Egypt.
Molecular identification of the fungal strain and phylogenetic analysis
The preparation of genomic DNA of Penicillium sp. strain HNSAM-7 was conducted in accordance with the methods described by Sambrook et al. [34]. DNA extraction, polymerase chain reaction (PCR), and 18S rRNA sequencing were performed by Macrogen Korea Company in Gasan-dong, Geumcheon-gu, Seoul, Korea (http://www.macrogen.com). The amplification of the ITS1 region from Penicillium sp. strain HNSAM-7 was carried out via PCR. Primers used were reverse primer ITS4 “5'-TCCTCCGCTTATTGATATGC -3'” and forward primer ITS1 “3'-TCCGTAGGTGAACCTGCGG-5'”. The amplification was carried out in 100 μl containing: “1 μL DNA, 10 μL of 250 mM dNTP's, 10 μL PCR buffer, 3.5 μL 25 mM MgCl_2_, 0.5 μL Taq polymerase, 4 μL of 10 pmoL (each) forward and reverse primer, and water were added up to 100 μL”. The components of the PCR reaction were mixed thoroughly. DNA amplification was carried out in the thermal cycles using the following PCR program: 10 min denaturation at 95 ºC, then 30 s of 35 amplification cycles at 95 ºC, annealing of 1 min at 55 ºC, extension of 1 min at 72 ºC, and 15 min final extension at 72 ºC, the number of cycles equals 35. The PCR reaction mixture was purified using a purification kit from Thermo (GeneJET™ PCR, K0701). The obtained sequence of the ITS1 region of Penicillium crustosum strain HNSAM-7 was analyzed using the basic local alignment search tool (BLAST) [35] (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=Blastearch&LINKLOC = blasthome) at the NCBI database, and the obtained sequence was compared with the related sequences of representative members of fungi retrieved from the Gen Bank, DDBJ, PDB, and EMBL databases. The ITS obtained sequence of the Penicillium sp. strain HNSAM-7 was deposited in the GenBank database under accession number OR783158. The phylogenetic tree was constructed via the neighbor-joining algorithm [36] using the software package MEGA-X [37].
CNPs biosynthesis
Low molecular weight chitosan (Poly-(1,4-b-D-glucopyranosamine)) was obtained from Bio Basic Inc. (Markham, Ontario L3R 8T4, Canada) and possessed a deacetylation degree of ≥ 90%, a molecular weight of 20 kDa, and a viscosity between 60 and 300. Chitosan was dissolved in 1% acetic acid (at a concentration of 1%, w/v) and kept under magnetic stirring for 24 h to ensure complete dissolution of chitosan in the solution. Then, pH was adjusted to 5 with 1N NaOH. The fungal strain was cultured in a 250-mL Erlenmeyer flask containing 50 mL of Czapek–Dox broth medium. The inoculated flasks were incubated at 30 ºC for 5 days. After the incubation period, the mycelia of the fungal strain were collected by centrifugation at 5000 × g for 15 min, and the cell-free supernatant was used for the production of CNPs according to the method of El-Naggar et al. [38]. Chitosan solution and cell-free supernatant of Penicillium crustosum strain HNSAM-7 were mixed in equal quantities (2 mL) at room temperature. The generated turbidity was centrifuged at 10000 × g for 10 min, washed, and finally freeze-dried for additional characterization.
Characterization of CNPs
To verify the formation of nanoparticles and to determine the maximum absorbance wavelength, the biosynthesized CNPs were analyzed using an Optizen Pop-UV–visible spectrophotometer. The CNPs solution was scanned at a wavelength range between 200 and 400 nm.
Samples of CNP’s size, structure, and morphological characteristics were analyzed using SEM (JSM—model JEOL-IT200) operating at 20 kV. Utilizing sputtering (SPI-Module), gold was applied to CNPs as a coating.
Transmission electron microscopy (HR-TEM, JEOL-2100, Japan) was used for imaging and investigating the CNPs. In order to do this, water (1:100 v/v) was added to 10 μL of the sample. After that, for 30 s, an amount of the specimen, about 10 μL, was applied to a carbon-coated grid of copper. Filter paper was used to remove extra liquid; afterwards, the grids spent no less than 24 h drying inside a desiccator [39].
Energy dispersive X-ray spectroscopy (EDX), which is accomplished with TEM, is widely used to determine the component configuration of a material. In order to learn more about the CNPs, the TEM’s electron beam was focused on a single nanoparticle using its program functions [38].
Finding out the structural characteristics of nanoparticles requires the use of XRD. Bruker (D2 Phaser—2nd Gen) has been utilized to measure X-ray diffraction using the radiation of CuKα (wavelength = 1.54) produced at 10 mA and 30 kV while operating at room temperature. Diffraction intensity was measured at a scanning rate of 2º min^−1^, and the measured values of 2θ ranged from 5º to 80º [40].
Investigating the surface properties of CNPs and the composition of each of them was documented using FTIR spectroscopy. To examine the surface characteristics, samples of CNPs were ground with KBr pellets. The CNPs’ FTIR spectrum has been determined using a Shimadzu FTIR-8400 S spectrophotometer with a resolution of about 1 cm^−1^ and a spectrum from 4000 to 500 cm^−1^.
The behavior of the suspended colloids or nanoparticles can be accurately predicted using the ζ-potential [11]. The ζ-potential and surface charge parameters of CNPs employed in this study were determined using Malvern 3000 analytical Zetasizer software (Nano ZS, UK), containing a laser Doppler and identified using phase analysis light scattering [38]. Deionized water was used to dilute the nanoparticles, with a viscosity defined as 0.887 cP to avoid numerous scattering effects; afterwards, it was placed within the sample cuvette [41]. Following the sample’s dissolution, 5.5 mm of calibrated space was used to count the nanoparticles at a count rate of 348.9 kcps (kilo counts per second) for 60 s. Before analysis, the diluted CNPs suspension was homogenized for 10 min at a speed of 13,000 rpm in a high-speed homogenizer, and then it was placed in an ultrasonic bath. The tests were performed on the sample three times at a temperature of 25 ºC [4]. The mean values for the average hydrodynamic diameter of various nanoformulations were computed.
The CNPs sample was placed in a platinum sample pan after withering at 60 ºC for 1 h. Employing a thermos-analyzer with model 50-H, the samples were subjected to temperatures that occur between room temperature and 800 ºC, and TGA has been performed. TGA was run at a flow rate of 10 mL min^−1^ in a nitrogen environment and at an increment of 10 ºC per minute. The temperature was plotted against the percentage of weight loss [38].
To evaluate the CNPs' pyrolysis pattern, DSC was performed. The CNPs sample was placed on an aluminum sample pan after drying at 60 ºC for 1 h. The test was conducted in a nitrogen environment at a heating temperature of 10 °C min^−1^ and a rate of flow of 30 mL min^−1^. The temperature range used to create the thermogram is from room temperature to 800 ºC. The thermogravimetric analysis results for the CNPs' first breakdown temperature were used to determine the DSC upper limit. Heat flow and temperature were plotted on the graph [38].
Optimization of green-synthesized chitosan nanoparticles via FCCCD
FCCCD was used to determine the optimal levels of fungal extract concentration (%, v/v), initial pH level, and chitosan concentration for producing the highest yield of CNPs using the cell-free supernatant of Penicillium crustosum strain HNSAM-7. Each variable was assessed using three coded levels (− 1, 0, and 1) [42]. The use of FCCCD allowed for the determination of the interaction, linear, and quadratic impacts of the process independent variables on CNPs biosynthesis. The following second-degree polynomial equation was used to determine the correlation between the process independent variables and the response value (Y, CNPs biosynthesis):
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Y\, = \,\beta_{0} \, + \,\sum\limits_{i} {\beta_{i} X_{i} \,} \, + \,\sum\limits_{ii} {\beta_{ii} X_{i}^{2} } \, + \,\sum\limits_{ij} {\beta_{ij} {X_{i}} {X_{j}} }$$\end{document}in which Y is the predicted CNPs biosynthesis (mg/mL), X_i_ corresponds to the coded values of the independent variables, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{0}$$\end{document} referred to a regression coefficient, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{i}$$\end{document} represents the linear coefficient, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{ii}$$\end{document} represents the quadratic coefficients, while \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\beta }_{ij}$$\end{document} corresponds to the interaction coefficients.
Statistical analysis
Design-Expert software (Version 12, Stat-Ease, Inc., Minneapolis, MN, USA) was used to generate a four-variable FCCCD with six central runs and to perform the statistical analysis. STATISTICA (Version 8, StatSoft, Inc., Tulsa, OK) was used to create three-dimensional surface plots [3].
Time–kill assay
For determining the impact of different doses of CNPs as a function of time during the incubation period in the fungal death process, such technique was utilized by macro-dilution broth assay based on the Clinical and Laboratory Standard Institute (CLSI) guideline. In brief, 100 μL of T. asahii suspensions (5 × 10^5^ CFU/mL) was inoculated into tubes of Sabouraud dextrose broth (2 mL) containing different doses of CNPs (50, 125, 250, 500, and 1000 μg/mL). Additionally, a control tube of fungal suspension lacking any treatment was run in parallel. In comparison, fluconazole and nystatin, as standard antifungal agents, were examined. Both control and treated tubes were incubated at 30 ºC and 150 rpm for 120 h. Then, aliquots from each culture were drawn at different time intervals (i.e., every 3 h.), cultivated onto Sabouraud dextrose agar plates, and incubated as formerly mentioned. After incubation, the total count of living cells (CFU/mL) was calculated in each tube at every time interval. Eventually, the plot of the time-kill assay was constructed by the logarithm of counted live colonies against time, for manifesting the changes of growth rate over time [43, 44]. Experiments were performed in triplicates, and Graphpad Instat software was used to statistically analyze the data and define the significant differences (P ≤ 0.05). To assess the change, either reduction or increase, in the fungal populations compared to the initial inoculums, the log 10 reduction and percentage decrease for each time point were considered according to the following equations [45]:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\% \,{\mathrm{Reduction}}\, = \,{\mathrm{Initial}}\,{\mathrm{count}}\,{-}\,{\mathrm{count}}\,{\mathrm{at}}\,{\mathrm{x}}\,{\mathrm{time}}\,{\mathrm{interval}}/{\mathrm{initial}}\,{\mathrm{count}}\, \times \,{1}00,$$\end{document} \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Log1}}0\,{\mathrm{reduction}}\, = \,{\mathrm{Log1}}0\,\left( {{\mathrm{initial}}\,{\mathrm{count}}} \right)\,{-}\,{\mathrm{Log1}}0\,\left( { \times \,{\mathrm{time}}\,{\mathrm{interval}}} \right)$$\end{document}Mathematical model for fungicidal activity
The following exponential Eq. 4 was employed to explain the killing kinetics of CNPs’ fungicidal activity by fitting the mean data at each time point:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{N}}_{{\mathrm{t}}} \, = \,{\mathrm{N}}_{0} \,{\mathrm{x}}\,{\mathrm{e}}^{{ - {\mathrm{Kt}}}} ,$$\end{document}where N_t_ represents the number of viable yeasts at time t, N_0_ is the number of viable yeast cells at the beginning of the experiment, K expresses the killing (or lethality) rate, and t is the exposure time. The exponential Eq. 5 was converted into a line by applying natural logarithms:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {{\mathrm{log}}\,{\mathrm{N}}_{{\mathrm{t}}} \, = \,{\mathrm{log}}\,{\mathrm{N}}_{0} \, - \,{\mathrm{Kt}}} \right).$$\end{document}The goodness of fit of the data was determined from the correlation coefficient (R^2^; ≥ 0.8). The fungicidal behavior is compared by K values, wherein a positive K-value indicates the growth and the negative values symbolize the killing [46]. Besides, the distinguishing between the fungicidal and fungistatic activity was defined as a decline of ≥ 3 log10 CFU/ mL from the initial inoculum and no change compared to the initial inoculum, correspondingly [47, 48].
Ultrastructure study of Trichosporon asahii upon CNPs treatment
The morphological alterations of examined fungus caused by sub-lethal dose of CNPs were scrutinized through scanning electron microscope (SEM). The treated and untreated samples were prefixed in 2.5% glutaraldehyde for 24 h at 4 °C and dehydrated with gradient ethyl alcohol series (30:100%) for 15 min. The dried samples were subjected to a Polaron SC7620 Sputter Coater for gold coating step and inspected using SEM (JEOL JSM 6360LA, Japan) [38].
A schematic representation of the research design framework of this study has been provided in Fig. 1.Fig. 1A schematic representation summarizes the study’s methodology, key findings, and the application of the biosynthesized CNPs
Results and discussion
Macroscopic and microscopic examination of Penicillium sp. strain HNSAM-7
Penicillium crustosum exhibited good growth on potato dextrose agar (PDA) plates, and its colonies showed greyish green with abundant sporulation and a floccose-like texture (Fig. 2A, B). Colonies were yellow to orange on the reverse side. Conidiophores are mostly terverticillate, rough-walled; and conidia were broadly ellipsoidal, smooth, thick-walled, and borne in defined or occasionally irregular columns. Scanning electron microscopy (SEM) has been used to show the fine features of Penicillium sp. strain HNSAM-7. The SEM micrographs in Fig. 2C–F showed rough-walled conidiophores bearing biseriate phialides carrying chains of spherical conidia with smooth to finely roughened walls.Fig. 2Penicillium crustosum growth on PDA plates A surface morphology of colonies and B Colonies reverse side, C–F scanning electron micrographs of Penicillium crustosum strain HNSAM-7 at different magnifications
Molecular identification and the analysis of the ITS region sequence
The ITS region of the Penicillium sp. strain HNSAM-7 was amplified, sequenced, and deposited in the GenBank database under accession number OR783158. The BLAST algorithm (https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastn&PAGE_TYPE=BlastSearch&LINK_LOC=blasthome) was used for comparative analysis of the obtained sequence of the ITS region of Penicillium sp. strain HNSAM-7 with those sequences of the reference species in the NCBI Nucleotide Database from the DNA DataBank of Japan (DDBJ), GenBank at NCBI, and the European Nucleotide Archive (ENA).
The ITS region sequence of Penicillium sp. strain HNSAM-7 had a significant identity to a number of Penicillium spp. The MEGA X software was used to construct the phylogenetic tree. The phylogenetic tree obtained by applying the neighbor-joining method is illustrated in Fig. 3. The phylogenetic tree exhibited that Penicillium sp. strain HNSAM-7 falls with Penicillium crustosum strain EXK D02 20 (OP592146.1) and Penicillium crustosum strain ZP-2 (KT192315.1) in the same clade with a similarity of 100%. Based on the study of the ITS region sequence and the strain’s morphological features, it should be identified as Penicillium crustosum strain HNSAM-7.Fig. 3. Phylogenetic tree of Penicillium sp. strain HNSAM-7 obtained by neighbor-joining analysis. The Tamura-Nei technique was used to calculate the evolutionary distances, which are expressed in terms of the number of base substitutions per site. For each sequence pair, all ambiguous locations were eliminated (pairwise deletion option). MEGA X was used to conduct evolutionary analysis
The biosynthesis and UV–visible spectrum for CNPs
Penicillium crustosum strain HNSAM-7 was cultured in a 250-mL Erlenmeyer flask containing 50 mL of Czapek–Dox broth medium (Fig. 4A). Following the incubation period, the fungal strain’s mycelia were separated by centrifugation at 5000 × g for 15 min, and the cell-free supernatant was utilized for the biosynthesis of CNPs. Equal volumes of the cell-free supernatant of Penicillium crustosum strain HNSAM-7 and chitosan solution were combined at room temperature. CNPs as polymeric nanoparticles appeared as turbidity in the reaction mixture (Fig. 4B). The UV/visible spectrum of the generated turbidity was measured. The UV/visible spectrum of chitosan and the biosynthesized chitosan nanoparticles (Fig. 4C), showing that the maximum absorbance wave lengths are 231 and 251 nm, respectively. This result is consistent with [49], who reported that a UV–vis absorption band for CNPs of 200–300 nm indicates the existence of a CO group in the CNPs.Fig. 4A Penicillium crustosum strain HNSAM-7 on Czapek-Dox broth medium; B 1: Vials of chitosan solution; 2: fungal free supernatant; 3: chitosan nanoparticles solution; C UV/visible spectrum of chitosan (blue curve) and the biosynthesized chitosan nanoparticles (red curve)
Electron microscopy investigation for CNPs
SEM and TEM are widely recognized as the primary analytical tools for detailed investigation of nanoparticles. The combination of these techniques allows for comprehensive analysis of particle size, shape, surface morphology, crystallinity, and other structural characteristics. TEM provides substantially two-dimensional resolution for precise size measurement, SEM accurately captures three-dimensional surface topology and detailed morphological features [38, 50, 51].
As depicted in Fig. 5A, scanning electron microscopy (SEM) was used at a magnification scale of 40,000 × to provide an overview of the morphology and analyze the size and surface morphological structure of the biosynthesized CNPs. The image revealed predominantly spherical nanoparticles with consistent shapes, smooth surfaces, and uniform size. Ghadi et al [52] observed the physical aggregation in similar chitosan nanoparticles. Similarly, most chitosan-derived nanoparticles in previous studies display a spherical morphology [53], with only a tiny percentage having an oval-pleated [54] or rod-shaped structure [55, 56]. In addition, the SEM images show that the CNPs are well-dispersed and intertwined, creating a larger surface area, which is advantageous for adsorption applications [55]. In comparison, the CNPs synthesized by Wardani and Sudjarwo's [57] exhibited a rough surface and spherical form with diameters around 500 nm. Khanmohammadi et al. [58] reported FE-SEM particle sizes for synthesized CNPs ranging from 33.64 to 74.87 nm. Asif et al. [59] emphasized that the particle size and properties of chitosan particles could be controlled by varying precursor concentrations and solvent conditions, producing sizes from nanometers to micrometers scale.Fig. 5SEM (A), particle size analysis (B), TEM (C) and EDX (D) micrographs of chitosan nanoparticles
The particle size distribution and TEM image presented in Fig. 5B&C. TEM image was taken at a 500-nm scale and provided higher-resolution insights into particle morphology and a more comprehensive understanding than SEM. TEM confirmed the spherical shape of the CNPs, with particle sizes ranging from 5 to 17 nm. Ghadi et al. [52] reported that the the synthesis of uniform spherical CNPs ranging from 10 to 80 nm.
These observations support the complementary role of SEM and TEM in CNPs’ characterization, as also noted by El-Naggar et al. [38] and Putri et al. [60]. Prior research indicates that the particle size of chitosan nanoparticles significantly influences their characteristics [4, 61].
TEM measurements of particle sizes in previous studies ranged from 60 to 280 nm [62], while Dudhani et al*.* [63] reported sizes around 110 ± 5 nm, and Zhang et al. [64] synthesized particles between 100 and 400 nm. The preparation process for CNPs has an impact on their particle size. For instance, the estimated size of the CNPs produced by the nanospray drier measured approximately 1000 nm, influenced by the spray cap diameter [65], while self-assembled CNPs ranged from 277 to 731 nm [66]. While Nguyen et al. [67] showed that both chitosan molecular weight and spray dryer nozzle dimensions affect CNP size, producing ranges between 166 and 1230 nm. The well-dispersed and intertwined arrangement of the biosynthesized CNPs increases surface area, enhancing their utility in adsorption-based applications [38, 55]. Based on the TEM image, the CNPs’ porous surface may improve their effectiveness as antibacterial nanomaterials in medicine and agriculture. This allows for the effective use of porous nature’s capacity to adsorb hazardous substances and repel microorganisms [51, 68]. Additionally, high-porosity also correlates with increased reactivity and a larger specific surface area [69].
Energy dispersive X-ray (EDX) analysis
CNPs may undergo structural modifications during the synthesis process, which can be detected via EDX analysis [38]. The elemental composition and purity of biosynthesized CNPs are studied using EDX coupled with TEM and shown in Fig. 5D. This technique provides quantitative information about the types, distributions, and concentrations of different elements. When the TEM electron beam interacts with the sample, it ejects inner-shell electrons, producing characteristic X-rays with intensities proportional to element concentration [70]. The EDX spectrum of CNPs revealed a uniform elemental component. The noise peak in the EDX spectrum is located at 0 keV. The four elements that make up chitosan, displaying hydrogen (H), carbon (C), nitrogen (N), and oxygen (O), are the primary constituents of chitosan. The consistency of these elements throughout the sample validates the structural stability of the nanoparticles during biosynthesis [38].
X-ray diffraction (XRD)
In materials research, the XRD pattern is employed as a quick and basic method for the phase identification of crystallinity to provide sufficient information about the unit characteristics. This has led to the designation of XRD as a fingerprint for a particular substance. The different beam dispersion angles and intensities were caused by the irradiation of X-rays [11]. As seen in Fig. 6A, the dried CNPs’ XRD pattern was detected with 2θ between 5º and 80º, 30 kV of generator voltage, and 10 mA of generator current and measured at room temperature [3]. Compared to pure chitosan, which exhibits peaks at 2θ = 12.281, 12.386, 20.212, and 29.155°, the biosynthesized CNPs displayed peaks at 11.8°, 25.36°, 52.84°, and 72.04°, reflecting the influence of the fungal extract on crystal formation. Furthermore, the prominent diffraction peak in the spectrum recognized at 2θ = 25.36º corresponds to the (110) plane of the crystalline structure of anhydrous chitin, and it is strong, demonstrating the chitosan’s high degree of crystallinity. While the weaker diffraction peak at 2θ of 11.8º in chitosan as well as CNPs’ reflects the (020) hydrated crystalline structure [3, 71]. Scherrer’s equation (Eq. 6) was used to calculate the size of the individual crystallite (t):
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$t\, = \,k. \lambda /\beta \cos \theta ,$$\end{document}where the Scherrer’s constant k is (0.89–0.9), the full width at half maximum (FWHM) is β, λ is the X-ray wavelength, and θ represents the Bragg angle.Fig. 6XRD (A), Zeta potential (B), and FTIR (C) analyses of chitosan nanoparticles
The sample crystallite size for the plan (110) was estimated using Eq. 6, and it was found to be around 5.5 nm. This result is approximately in agreement with Thamilarasan et al. [72], who fabricated chitosan nanocrystals in a single step using Penaeus with a crystallite size of 6.9 nm. This suggests that the biosynthetic method promotes nanocrystalline structures with high surface reactivity, differing from the native chitosan crystalline arrangement.
Zeta (ζ) potential analysis
The ζ-potential value of the biosynthesized CNPs was measured, which is one of the key elements needed to maintain equilibrium in an aqueous nanosuspension [73]. The ζ-potential reflects particle surface charge and governs the degree of repulsion among similarly charged particles in suspension [11, 74]. Chandrasekaran et al. [75] found that cellular interaction with charged ions or molecules is employed using ζ-potential. As a consequence, positively charged ions enhance the surface’s ζ-potential, whereas negatively charged ions lower it. By delivering through two electrodes a voltage at both sides of a cell containing the particle dispersion, the surface charge of CNPs was ascertained from ζ-potential. Particles with charges are attracted towards the electrode that has the opposing charge [40]. The biosynthesized CNPs exhibited a single peak at + 26.2 ± 4.22 mV (Fig. 6B) at 25 °C, indicating positive surface charge and good dispersion stability, consistent with Hussein et al. [76]. The positive charge of CNPs arises from protonated amine groups on chitosan [11], enhancing the interaction of CNPs with negatively charged microbial membranes or biomolecules. It was found that the inhibitory effect of CNPs was regulated by both their particle size and ζ-potential, which ranged from + 22 to + 55 mV [77]. A high ζ-potential value confers stability on small particles and molecules and prevents the agglomeration of nanoparticles in solutions or dispersions. On the other hand, if attraction forces are too strong at low potentials, flocculation may occur and the dispersion may collapse. Therefore, high negative or positive ζ-potential colloids have the advantage of being electrically stable compared to colloids with low ζ-potentials, which have a tendency to cause coagulation or flocculation [11, 78]. Considering an antimicrobial perspective, the positive charge of ζ-potential causes particles to interact easily with a biological system’s negatively charged cell membrane and/or DNA and can subsequently be discharged easily into the cytoplasm of the cell [11, 79]. The polydispersity index (or PDI), which has values between 0 and 1, measures the particle size distribution. The PDI value in this investigation is about 0.454 (Supplementary Figure 1), which is lower than 0.5, indicating moderate homogeneity and controlled particle size distribution, as values above 0.5 generally signify significant heterogeneity. Manimaran et al. [80] found that the PDI value, which is greater than 0.5, demonstrated considerable heterogeneity.
Fourier-transform infrared (FTIR) investigation
FTIR analysis was used to characterize and identify the different functional groups involved in the biosynthesis of CNPs and stabilization, which were made from the cell-free supernatant of Penicillium crustosum strain HNSAM-7. As shown in Fig. 6C, the measurements of the adsorbent spectra were made between 4000 and 500 cm^−1^, and the FTIR spectra show numerous peaks. Broad bands between 3400 and 3800 cm⁻^1^ correspond to O–H and N–H stretching, including intramolecular hydrogen bonding [3, 81]. The combined peaks of the stretching vibrations of the O–H stretching of alcoholic and phenolic groups and the stretching vibrations of -NH_2_ groups in CNPs are responsible for the band at 3427.62 cm^−1^ [11, 82], slightly shifted from the chitosan reference at 3442.09 cm⁻^1^ (OH groups stretching vibrations), indicating the involvement of these groups in the biosynthesis of CNPs.
The aliphatic stretching group (CH and CH_2_) appeared at 2929 cm⁻^1^ [3]. The stretching vibrations of C = C conjugated and C≡C have appeared at 2129.48 cm^−1^ [11]. The peak at 1635.69 cm^−1^ is ascribed to the C = O stretching of the amide I group (CONH_2_), which shifted from the chitosan standard amide I group at 1654.98 cm^−1^ [83], confirming interactions between protonated amine groups and fungal metabolites in the cell-free supernatant of Penicillium crustosum strain HNSAM-7. CNPs’ formation is demonstrated by the vibrations shifting from higher wave numbers to lower ones [3, 82]. The appearance of Amide III was denoted by bands at 1391.69 and 1310.67 cm^−1^ in the FTIR spectra of CNPs, indicating partial deacetylation and crosslinking. Chitosan deacetylation causes the stretch vibration in the oxygen bridge of CO groups (COH and COC), which results in absorption with a wavenumber around 1084.99 cm^−1^ [3]. The peaks in the range of 1000 and 1050 cm^−1^ demonstrate the stretching vibrations of C–OH and C–O–C [84]. Furthermore, the stretching vibration of C–O is responsible for spectral peaks in the 848–949 cm^−1^ region, which point to the presence of mannuronic and uronic acids [85]. The vibration of -C≡CH is responsible for the absorption peak at 644 cm^−1^ [86]. Flórez-Fernández et al. [87] speculate that peaks at about 600 cm^−1^ may have undergone either symmetric or asymmetric O = S = O deformation. The peak at 563.23 cm^−1^ indicates out-of-plane NH and C–O bending [88]. These functional groups reflect chitosan’s native structure [89]. The wiggling of the chitosan’s saccharide structure is shown by the tiny peaks at the end of the FTIR spectrum [90]. A capping agent, which functions as a stabilizer to suppress nanoparticle overgrowth and prevent their aggregation and/or coagulation in colloidal synthesis, is indicated by the presence of a variety of strong bands in the FTIR spectrum [11].
Thermal behavior of CNPs
TGA (thermogravimetric analysis) and DSC (differential scanning calorimetry) were the two main methods used to find out the thermal properties of CNPs made by biosynthesis. Thermogravimetric analysis, or TGA, evaluates how the chemical and physical properties of a material change as a result of a continuously varying heating rate [91]. The thermal properties of a sample of 2.362 mg of biosynthesized CNPs were examined using a thermogravimetric analyzer, model TGA-50H. At temperatures ranging from ambient temperature to 800 ºC, the sample was processed at flow rates of 40 mL/min [3]. With the change in temperature, the change in CNPs mass was observed and investigated, as shown in Fig. 7A. The nature of chemical processes is reflected in the mass variation of CNPs [11]. Figure 7A shows the TGA of CNPs, which shows five stages of degradation, shown in a step-like pattern. The percentage losses are 10.30% at ambient temperature to 67.80 ºC, 11.01% from 67.80 ºC to 141.68 ºC, 45.03% at 141.68 ºC to 366.88 ºC, 7.458% at 366.88 ºC to 491.20 ºC, and 7.984% at 491.20 ºC to 788.15 ºC. At temperatures between 141.68 and 366.88 ºC, chitosan thermal degradation caused the greatest weight loss (45.03%), which was 1.064 mg, while the lowest weight loss (7.458%) was observed over the 366.88–491.20 ºC temperature range. According to El-Naggar et al. [11], drying occurs in the temperature range of 31.20–101.5 ºC, which causes the initial mass drop at the start of heating. This is because the two polar groups’ water-bound components have disappeared and were caused by the depolymerization, decomposition of volatile products, and saccharide rings’ dehydration process, which does not cause any chemical reactions or structural changes [4, 56]. While Sivakami et al. [56] discovered that the weight loss that results from water evaporation happens between 50 and 150 ºC and the weight loss that results from the heat degradation of CNPs occurs between 200 and 350 ºC, also, this loss is a result of the intra- and intermolecular moisture in the CNPs evaporating. These weight losses showed that CNPs were partially thermally disintegrated. Despite CNPs’ reasonable heat constancy, they maintained a respectable amount of mass, about 18%, at a heating temperature of 800 ºC. Crosslinking has made the hydrogel network stronger and more rigid, as seen by the increase in heat stability [38, 55].Fig. 7TGA (A) and DSC (B) analyses of chitosan nanoparticles
The thermal analysis processes were not combined during dynamic TGA, although there is still a chance that they could have interfered inadvertently, requiring either much slower heating rates or stepwise TGA techniques. As a consequence, determining the identification of suspected decomposition products frequently requires chemical testing, such as DSC, in addition to TGA. This is because TGA alone may not be adequate to identify the decomposition products [4, 54].
Investigations using TGA and DSC were both carried out, and each is a measurement of the thermo-analytical properties required to define the nanoparticles’ characteristics that participate in chemical processes across a controlled temperature range. TGA investigates the differential thermal analysis as a response of time with a steady temperature and/or a steady mass loss, or as a response of the sample’s mass change in relation to temperature variations. DSC, in contrast, assesses the heat flow demanded or produced in relation to the temperature change at a specific moment. The process for determining the alterations in samples that are brought on by heat is the fundamental difference between TGA and DSC [11, 92].
Different heating rates were used during the thermo-analytical examination using differential scanning calorimetry (DSC) to measure the percentage variation (positive or negative) in the CNPs’ heat flow as a result of temperature, using the solvent as a reference. At identical temperatures, the experiment was carried out using both CNPs and solvent. The temperature gets higher linearly with time in the sample holder. Because of this, evidence for physical phenomena like thermal stability and purity can be provided [11]. This technique is used to quantify how temperature affects chemical reactions and phase transitions [38]. Figure 7B illustrates the results of the DSC investigation at different flows of heating to highlight the extent of temperature-related fluctuation in the CNPs’ heat flow. One endothermic peak could be seen on the CNPs' DSC thermogram, requiring heat of 1868 mJ of CNPs, and was found at 76.15ºC due to the modification of the thermodynamic system. This finding agrees with those of Zhao et al. [93], who discovered that the blank chitosan nanoparticle’s DSC curve displayed an endothermic peak at a temperature close to 90 ºC. As well, this outcome is consistent with the findings of Vijayalakshmi et al. [55], who observed that a wide endothermic peak attained below 80ºC is exceptional for the elimination of absorbed water over time [11]. The biosynthesized CNPs undergo an exothermic reaction with heat, indicating that the particles were produced through crystallization [38].
Application of FCCCD to optimize chitosan nanoparticles' biosynthesis
Response surface methodology (RSM) was employed to estimate the optimal levels of variables impacting the green synthesis of CNPs. Face-centered central composite design (FCCCD) is a popular experimental design technique in RSM that has been used to optimize CNPs biosynthesis [38]. Twenty experimental runs were used for the experimental design in this investigation, which consisted of six center points, six axial points, and eight factorial points. The levels of the three variables used, including cell-free supernatant concentration of Penicillium crustosum (X_1_), initial pH level (X_2_), and chitosan concentration (X_3_), are displayed in Table 1. Run order 10 shows the maximum production of chitosan nanoparticles under the conditions of 100 (%, v/v) of cell-free supernatant concentration, initial pH (5), and 1.5 (%, w/v) of chitosan concentration; the maximum production was 10.69 mg/mL.
The minimal production of chitosan nanoparticles was 0.14 mg/mL in run order 11, which was attained under the conditions of 0.5 (%, w/v) chitosan concentration, initial pH level (5), and 100 (%, v/v) cell-free supernatant concentration. The minimal production was 0.14 mg/mL. Table 1 additionally includes predicted and experimental values.Table 1FCCCD matrix mean actual and predicted values of the green synthesis of CNPs using Penicillium crustosum strain HNSAM-7StdRunTypeX_1_X_2_X_3_Chitosan nanoparticles green synthesis (mg/mL)ActualPredictedResiduals111Axial0−106.466.62−0.15202Center0006.936.600.3323Factorial1−1−10.260.170.09184Center0006.896.600.29165Center0006.466.60−0.14136Axial00−11.662.30−0.64107Axial1006.977.17−0.20198Center0006.936.600.33179Center0006.506.60−0.10810Factorial11110.6910.76−0.07311Factorial−11−10.300.140.16712Factorial−1110.940.910.031513Center0006.866.600.261414Axial0017.607.450.15515Factorial−1−111.861.95−0.09916Axial−1002.572.86−0.291217Axial0106.196.52−0.33118Factorial−1−1−11.571.390.19619Factorial1−119.689.71−0.03420Factorial11−11.221.010.21VariableVariable code−101Cell-free supernatant conc. (%, v/v)X_1_5075100Initial pH levelX_2_44.55Chitosan concentration (%, w/v)X_3_0.511.5
Multiple regression analysis and ANOVA
The relationship between CNPs green synthesis and the independent variables is determined by multiple-regression statistical analysis and the analysis of variance (ANOVA) as depicted in Table 2. The ANOVA of FCCCD demonstrated that the model used for CNPs green synthesis was highly significant, as was evident from the model F-value of 182.74 with a very low P-value of < 0.0001 (Table 2). P-values that are less than 0.05 reveal the model’s significance. Also, factors evidencing P-values less than 0.05 were considered to have significant effects on the green synthesis of CNPs. It can be seen from the degree of significance that the linear coefficients of the cell-free supernatant concentration, chitosan concentration; interaction between the cell-free supernatant concentration, the initial pH level, and the cell-free supernatant concentration, chitosan concentration, quadratic effect of the cell-free supernatant concentration and chitosan concentration are significant. Additionally, the P-values of the coefficients indicate that, of the three factors under study, the interaction between the cell-free supernatant concentration, chitosan concentration had a very significant effect on the green synthesis of CNPs by the cell-free supernatant of Penicillium crustosum strain HNSAM-7 with a P-value of < 0.0001. This indicates that 99.9% of the model is impacted by both cell-free supernatant concentration and chitosan concentration. Moreover, the linear coefficients of the initial pH level (P = 0.6662), interaction between the initial pH level and chitosan concentration (P = 0.6892); quadratic effect of the initial pH level (P = 0.8836) had a non-significant effect on the green synthesis of CNPs by the cell-free supernatant of Penicillium crustosum strain HNSAM-7 and do not promote the synthesis.Table 2. Analysis of variance for the green synthesis of CNPs using Penicillium crustosum strain HNSAM-7Source of varianceSum of squaresdfMean square*F-valueP-value Prob > FCoefficient estimateModel200.07922.23182.74 < 0.00016.60Linear effectsX_1_46.54146.54382.54 < 0.00012.16X_2_0.0210.020.200.6662−0.05X_3_66.40166.40545.80 < 0.00012.58Interaction effectsX_1_ X_2_2.1812.1817.880.00170.52X_1_ X_3_40.34140.34331.58 < 0.00012.25X_2_ X_3_0.0210.020.170.68920.05Quadratic effectsX_1_^2^6.9216.9256.88 < 0.0001−1.59X_2_^2^0.0010.000.020.8836−0.03X_3_^2^8.2118.2167.47 < 0.0001−1.73Error effectsLack of Fit0.9750.1940.0771Pure Error0.2450.05Std. Dev0.35R^2^0.994Mean4.93Adj R^2^0.9885C.V. %7.08Pred R^2^0.9697PRESS6.11Adeq precision43.03^^ Significant values, F Fisher’s function, P level of significance, C.V coefficient of variation
The coefficients estimate values, either positive or negative, indicate that the factor has a large effect on the response, whereas the near zero effect means that the variable has little or no effect [38]. The response increases at an elevated level for every variable that has a positive coefficient estimate sign [94, 95]. If the coefficients estimate sign is negative, it is implied that the response increases when the variable is present at its low level. The green synthesis of CNPs is affected to varying degrees by each factor, as indicated by its coefficient. Furthermore, it was evident that the cell-free supernatant conc. (%, v/v), chitosan concentration (%, w/v) had positive effects on the green synthesis of CNPs (the coefficient values are 2.16 and 2.58, respectively), which means that the green synthesis of CNPs increases with increasing levels of both cell-free supernatant and chitosan concentration. Among the non-significant variables, the initial pH level has a negative effect on the green synthesis of CNPs (the coefficient value is -0.05), which means that the increase in the cell-free supernatant concentration, chitosan concentration, and decrease in the initial pH level could exert a positive effect on the green synthesis of CNPs.
The model’s goodness of fit was evaluated using the determination coefficient (R^2^). A regression model is considered to have a very strong correlation if it has an R^2^-value that is greater than 0.9 [96]. According to the findings of our study, the value of the determination coefficient (R^2^ = 0.994) revealed that 99.40 percent of the variability in the green synthesis of CNPs by the cell-free supernatant of Penicillium crustosum strain HNSAM-7 was attributed to the studied independent variables, while only 0.60 percent of the overall variations could not be explained by the model. Furthermore, the adjusted determination coefficient’s value (adj. R^2^ of 0.9885) was also extremely high, which guarantees that the model will have a high significance (Table 2). The adj. R^2^-value increases exclusively when a new variable improves the model’s fit. Adj. R^2^ value decreases when the variable fails to substantially improve the fit of the model significantly. The predicted (Pred. R^2^) value of 0.9697 was in reasonable agreement with the value of adjusted-R^2^ (adj. R^2^ of 0.9885), which indicated a good match between all of the actual values of CNPs and their predicted values. The R^2^-prediction is a measure of how accurately the model predicts responses for future experiments. As a result, our findings demonstrated that the green synthesis of CNPs is a predictable process with an accuracy of 96.97 percent.
The signal-to-noise ratio (Adeq. Precision) is better when it is greater than 4. The Adeq. precision is 43.03, which is greater than 4, indicating that this model could be utilized to navigate the design space of precision [97]. The residual variance of the data in relation to the size of the mean is measured by the coefficient of variation percentage, or C.V.%. A larger C.V.% usually indicates a lower level of the experimental accuracy [98]. The mathematical analysis of the green synthesis of CNPs reveals a coefficient of variation percentage (C.V.%) of 7.08%, which is relatively small and suggests that experimental trials were precise.
The second-order polynomial model was used to assess the correlation between the independent variables and the biosynthesis of chitosan nanoparticles. The second-order polynomial model was also used to estimate the maximum green synthesis of CNPs by the cell-free supernatant of Penicillium crustosum strain HNSAM-7 at the optimal levels of the three independent variables: the cell-free supernatant concentration of Penicillium crustosum strain HNSAM-7 (X_1_), the initial pH level (X_2_), and the chitosan concentration (X_3_). The following second-order polynomial mathematical model was applied to describe the predicted green synthesis of CNPs in terms of the independent process variables:
\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\mathrm{Y}}\, = \,{6}.{6}0\, + \,{2}.{16}\,{\mathrm{X}}_{{1}} \, - \,0.0{5}\,{\mathrm{X}}_{{2}} \, + \,{2}.{58}\,{\mathrm{X}}_{{3}} \, + \,0.{52}\,{\mathrm{X}}_{{1}} {\mathrm{X}}_{{2}} \, + \,{2}.{25}\,{\mathrm{X}}_{{1}} {\mathrm{X}}_{{3}} \, + \,0.0{5}\,{\mathrm{X}}_{{2}} {\mathrm{X}}_{{3}} \, - \,{1}.{59}\,{\mathrm{X}}_{{1}}^{{2}} \, - \,0.0{3}\,{\mathrm{X}}_{{2}}^{{2}} \, - \,{1}.{73}\,{\mathrm{X}}_{{3}}^{{2}}$$\end{document}The fit summary results are displayed in Table 3. The highly significant and appropriate polynomial model for the green synthesis of CNPs was chosen based on the fit summary statistics results. This model must have a very low probability value, lower standard deviation, higher adjusted and predicted R-squared, and insignificant lack of fit test. The findings of the fit summary (Table 3) demonstrated that the quadratic model is a highly significant and appropriate polynomial model fitting the green synthesis of CNPs by the cell-free supernatant of Penicillium crustosum strain HNSAM-7. The quadratic model has the lowest probability value (P < 0.0001) and the highest adjusted and predicted R-squared values of 0.9885 and 0.9697, respectively. The quadratic model also had non-significant lack of fit (P = 0.0771 and F-value 4). The quadratic model also showed the smallest standard deviation of 0.35 and sum of squares of prediction error of 6.11.Table 3. Fit summary of FCCCD for the green synthesis of CNPs using Penicillium crustosum strain HNSAM-7Fit summarySourceSequential* P*-valueLack of fit P-valueAdjusted R^2^Predicted R^2^Linear0.0036 < 0.00010.47890.1112FI0.0315 < 0.00010.6675−0.5151Quadratic < 0.00010.07710.98850.9697Sequential model sum of squaresSourceSum of squaresDfMean squareF-valueP-valueProb > FLinear vs mean112.96337.656.820.00362FI vs linear42.53314.184.020.0315Quadratic vs 2FI44.58314.86122.15 < 0.0001Lack of fit testsSourceSum of squaresDfMean squareF-valueP-valueProb > FLinear88.08118.01164.65 < 0.00012FI45.5585.69117.08 < 0.0001Quadratic0.9750.194.000.0771Model summary statisticsSourceStandard deviationR-squaredAdjusted R-squaredPredicted R-squaredPRESSLinear2.350.56120.47890.111178.932FI1.880.77250.6675−0.5151304.97Quadratic0.350.9940.98850.96976.11“ Significant values, df: degree of freedom, PRESS: sum of squares of prediction error, two factors interaction: 2FI”
Model adequacy check
The normal probability plot (NPP), a valuable diagnostic tool, is used to assess the adequacy of the model and to achieve an accurate prediction of the response [99, 100]. The normality of the experimental results was assessed using the NPP. When the experimental results are plotted against the theoretical predicted results, the points must approximate a straight line. Any deviations from this straight line are indicative of deviations from the normal distribution.
The variance between the experimental data of the response and their predicted theoretical values is referred to as the residual [101, 102], and a small residual indicates a high degree of model precision. The normal probability plot (Fig. 8A) displayed the externally studentized residuals versus the normal % probability. The points are located in close proximity to the diagnostic line, which indicates that the experimental results have a normal distribution and the adequacy of the model to achieve an accurate prediction for the green synthesis of CNPs by the cell-free supernatant of Penicillium crustosum strain HNSAM-7 [103, 104].Fig. 8A Normal probability plot of internally studentized residuals, B plot of internally studentized residuals versus predicted values, C plot of predicted versus actual and D Box-Cox plot of model transformation of cell free supernatant concentration (%, v/v) (X_1_), initial pH level (X_2_), chitosan concentration (X_3_) on CNPs bio-synthesized using Penicillium crustosum strain HNSAM-7
The predicted values are presented on the X-axis in Fig. 8B versus the externally studentized residuals on the Y-axis. The residual points almost all fell along the 0 line, indicating that the experimental and predicted values are close together [105]. The experimental versus theoretical predicted results for the green synthesis of CNPs by the cell-free supernatant of Penicillium crustosum strain HNSAM-7are displayed in Fig. 8C. The points are close to the diagnostic line, indicating high agreement between the experimental and theoretical results and confirming the validity of the model [106].
The Box–Cox plot of the model transformation (Fig. 8D) shows the current λ value (λ = 1) as a blue line, while the value (λ best = 0.73) is shown as a green line. The minimum and maximum values of the 95% confidence interval are shown as red lines (0.48 and 1.04, respectively). The ideal zone represented by the blue line (λ = 1) lies between the values of the minimum and maximum confidence level (0.48 and 1.04 respectively), meaning that no data transformation is needed [107].
The three-dimensional surface plots (3D plots)
The three-dimensional surface plots shown in Fig. 9A–C were used to investigate the relationship between pairwise combinations of the three independent bioprocess factors (chitosan concentration, the initial pH level, and the cell-free supernatant concentration of Penicillium crustosum) and their effects on the green synthesis of CNPs and their optimal levels. On the Z-axis, the green synthesis of CNPs was plotted against two independent factors that were allowed to vary while the third independent variable remained constant at its midpoint (zero level).Fig. 9A–C 3D plots showing the mutual effects of cell free supernatant concentration (%, v/v) (X_1_), initial pH level (X_2_), chitosan concentration (X_3_) on CNPs bio-synthesized using Penicillium crustosum strain HNSAM-7
The effects of an initial pH level when interacting with the cell-free supernatant concentration on the green synthesis of CNPs, whereas the chitosan concentration was held at its zero levels (1%), are illustrated in Fig. 9A. As shown in Fig. 9A, both low concentrations of cell-free supernatant and initial pH level decrease the green production of CNPs. As the cell-free supernatant concentration and initial pH level increased, the green synthesis of CNPs increased gradually until it reached its optimum value, after which point it declined. The highest biosynthesis of CNPs occurred at an initial pH of around 5 and the cell-free supernatant concentration around 100% on the green synthesis of CNPs. Furthermore, it was evident that the interaction effects between the initial pH level and the cell-free supernatant concentration had positive effects on the green synthesis of CNPs as it is significant and has a positive coefficient (Table 2).
According to a study by Shu and Zhu [108], the synthesis of CNPs involved an electrostatic interaction between chitosan and polyanions, which was found to be pH-dependent in the mixing solution. According to Zohri et al. [109], pH has a significant impact on the size of chitosan NPs. The electric charge density of CNPs seems to change depending on the pH of the chitosan solution, which is a weak polyelectrolyte. According to Liu and Gao [110], pH has an impact on the zeta potential and particle size of CNPs. Because the amine groups in chitosan became protonated in acidic conditions, there was a lot of charge repulsion and molecule extension [111, 112]. It was proposed that the amino group-induced positive surface charge of chitosan is shielded due to restructuring of the molecular structure or/and adsorption of additional negatively charged ions at low pH [61].
Figure 9B displays the effects of the cell-free supernatant concentration when interacting with the concentrations of chitosan on the green synthesis of CNPs, whereas the initial pH level remains unchanged at its zero level. The green synthesis of CNPs was gradually increased by increasing the concentrations of both chitosan and cell-free supernatant. Additionally, because the interaction between the concentrations of chitosan and the cell-free supernatant was significant and had a positive coefficient, it was clear that the interaction between the concentrations of chitosan and the cell-free supernatant had a favorable impact on the green synthesis of CNPs (Table 2). The findings are in agreement with those of Vaezifar et al. [49], who found that the optimal initial chitosan concentration for synthesizing the lowest diameters of CNPs was approximately 1.5% of chitosan concentration, as compared to higher concentrations. To synthesize chitosan nanoparticles, other researchers used varying chitosan concentrations such as 2% [92], 0.5% [113], and 0.8 mg/mL [114].
Many studies have been conducted on the genus Penicillium in the fields of biology, biotechnology, agriculture, and drug development. Alkaloids, polyketides, terpenoids, lactones, and steroids are among the compounds that have been successfully extracted from Penicillium species [115]. These substances possess bioactivities that include antibacterial, immunosuppressive, anti-inflammatory, anticancer, and antioxidant properties. The most attractive compound is terpenoids. Terpenoids are generally considered to be plant or fungal metabolites [116]. Terpenoids are classified as phenols, epoxides, alcohols, aldehydes, esters, ethers, and ketones [117].
One of the most often used self-assembly (chemical method) methods for polymeric materials to produce nanoparticles is desolvation. Using the desolvation approach, an aqueous solution of the biomolecule can be mixed with a desolvation agent, like poor solvents (often acetone, alcohols, aldehydes, or acetonitrile), to form the polymeric nanoparticles [118]. So, the naturally occurring alcohols, aldehydes, and ketones in the terpenoids presented in the cell-free supernatant of Penicillium crustosum strain HNSAM-7 could be responsible for the biosynthesis of the chitosan nanoparticles. The desolvation approach always needs two steps in nanoparticle synthesis, while in the biosynthesis of chitosan nanoparticles, it needs a one-step nature, non-toxicity, energy efficiency, and environmental friendliness. Moreover, biosynthesized CNPs exhibit greater stability [13].
Figure 9C displays the interaction between the initial pH level and chitosan concentration, whereas cell-free supernatant remained constant at its zero level. As shown in Fig. 9C, as the chitosan concentration and initial pH level increased, the green synthesis of CNPs increased gradually until it reached its optimum value, after which point it declined. The highest biosynthesis of CNPs occurred at an initial pH of around 5 and chitosan concentration of around 1.5%.
Desirability function analysis
Desirability function (DF) is one of the most popular methods for finding out the optimal predicted conditions that would yield the maximum value of the response [119, 120]. DF has values between 0 and 1. The optimal conditions for maximum CNPs biosynthesis were determined theoretically (Fig. 10) using the desirability function and confirmed through experimentation.Fig. 10. The optimization plot displays the desirability function and the optimum predicted values of CNPs bio-synthesized using cell free supernatant concentration of Penicillium crustosum strain HNSAM-7
The theoretically predicted optimal conditions for maximum CNPs biosynthesis using the cell-free supernatant of Penicillium crustosum strain HNSAM-7 were determined to be: an initial pH of 4.99, a chitosan concentration of 1.19 (percent, w/v), and a cell-free supernatant of 99.94 (percent, v/v) and the desirability function reached 1. The theoretically predicted highest CNPs biosynthesis was 10.71 mg/mL. It was verified through experiments that the CNPs biosynthesis was 10.53 mg/mL under these certain conditions. The verification demonstrated that the model had a high degree of precision as the theoretically predicted value of CNPs biosynthesis (10.71 mg/mL) was closer to the experimental value (10.53 mg/mL).
Time–kill assay
For perceiving the time required by CNPs to kill the examined building-deteriorating fungus (i.e., T. asahii), a time-kill assay was performed. Broadly, the substantial target of the time–kill assay is assessing the reduction of microbial growth upon contact with different concentrations of the antimicrobial agent in a multi-time point assay [121]. As an inceptive step, the inoculum of T. asahii was incubated with 50, 125, 250, 500, and 1000 µg/mL of CNPs for 120 h; thereafter, it was plated on SD agar plates every 3 h to determine the number of viable colony forming units (CFU/mL). Notably, all growth stages (i.e., lag, log, and stationary stage) were displayed in the untreated control sample by around 0.395 log_10_ CFU after 24 h of incubation, reaching the maximum at 120 h by 4.406 log_10_ CFU, implying a slower growth performance compared to other fungal strains like Candida sp. As shown in Fig. 11, the CNPs displayed cell death in a time/dose-dependent modality, wherein no killing or insignificant antifungal activity at low concentration (i.e., 50 µg/mL) of CNPs was noticed. Upon uplifting the concentration to 125 µg/mL, the cells of T. asahii grew slightly below the control, especially at early hours of incubation, while a progressive reduction in the number of viable cells was observed over time beginning from 66 h and reaching the maximum by 0.6 log_10_ reduction (11%) at 120 h, reflecting the fungistatic activity. However, the significant (P < 0.0001 and < 0.001) fungicidal potentiality of CNPs was recorded at concentrations of 250 and 500 µg/mL without a log phase by log_10_ reductions of 4.5 (83.22%) and 5.11 (92.9%) at 21 and 33 h with complete lethality occurring after 24 and 36 h of contact, respectively, unveiling the rapid fungicidal activity performance of both concentrations (Fig. 11A, B). Comparatively, 20 and 40 µg/mL of fluconazole and nystatin, respectively, frustrated the fungal growth entirely within the first 20 h. Seemingly, no fungal regeneration was detected upon extending the incubation time to 120 h. In this regard, the relationship between CNPs concentrations and the lethality rate (K-value), with R^2^ of 0.996, is denoted in Fig. 11C. The highest K values were obtained by −0.12 and −0.07 CFU/mL/h, with significance recorded (P < 0.0001) at CNPs concentrations of 250 and 500 µg/mL. In the same context, the generation time of T. asahii during the exponential phase was significantly higher (P < 0.05) than the corresponding generation time of the untreated control sample (6.36 ± 0.11 h), which recorded 7.68 ± 0.67 and 22.1 ± 5.2 h for 50 and 125 µg/mL, respectively. Additionally, the growth dynamics of T. asahii denoted a general decrease in the fungal growth rate with increasing the applied dose of CNPs (Fig. 11D). Collectively, all these evidences emphasized the adverse impact of CNPs on the cells of T. asahii. On the other hand, oddly, the highest concentration of CNPs (1000 µg/mL) did not exhibit any antagonistic capacity as deduced from the time–killing curve, despite the increase in generation time (6.85 ± 0.28) that was detected. This result could be attributed to the aggregation and agglomeration of CNPs to the limit that prevent their penetration to the fungal cells. Interestingly, these results agree with those found by El-Naggar et al. [11].Fig. 11. The effect of CNPs on fungal growth of Trichosporon asahii. A*-*Typical pattern of kill curves on Trichosporon asahii at different concentrations of CNPs (µg/mL) as a function of treatment time (h), B log_10_ reduction and killing percentage of each examined concentration, C-The relationship between K-value and each examined concentration revealing lethality and D-The relationship between growth rate and different CNPs concentration
Intriguingly, there were discrepancies in killing time and growth rate reduction of different fungal species examined among numerous published studies. That could be ascribed to the entire differences in the microbial physiological capabilities, metabolic performance and their resistance/susceptibility profiles along with the variations in cell wall entity and their building moieties [122]. In general, the chitin is a fundamental constituent of the fungal cell wall structure; nevertheless, the conjugation of various entities led to differences in the interaction’s dynamics with CNPs. Noteworthy mentioning that genus Trichosporon is characterized by the presence of glucuronoxylomannan, which is a cell wall-associated polysaccharide that plays the main role in the tolerance to antifungal agents and harsh environmental circumstances [123]. However, the higher negative charges of T. asahii might facilitate the rapid and potent electrostatic interaction with positively charged CNPs, which resulted in enhanced permeability and adsorption, followed by higher cellular damage in a shorter a considerable interval.
Ultrastructure study of Trichosporon asahii upon CNPs treatment
Herein, the recruitment of complementary microscopic means like SEM furnished a deeper vision to visualize the morphological alterations in T. asahii cells consequential to CNPs exposure. As depicted in Fig. 12, the fungal micromorphology appeared as an intricate network of spatially dispersed, long, branched, septated true and pseudohyphae with a healthy appearance and smooth cell surface, which were fragmented into rectangular to elongate oval shape arthroconidia (4–6 μm) (blue arrows). Besides, a thick compact slimy monolayer of polymeric material comprised numerous globose, ovoidal, and ellipsoidal shape blastoconidia (2–3.5 μm) (red arrows) was observed forming biofilm (yellow arrows), which is considered a characteristic virulence feature in pathogenic strains and antifungal mechanism property as well [124].Fig. 12SEM micrographs of T. asahii showing the morphological alterations induced by utilizing mycosynthesized CNPs. A—Control untreated cells, B—CNPs-treated cells. Blue arrows: arthroconidia, red arrows: blastoconidia, yellow arrows: biofilm polymeric matrix, green arrows: septa of true hyphae, orange arrows: deformed hyphae, magenta arrows: distorted blastoconidia and dashed yellow arrows: CNPs
On the other hand, the treatment with CNPs induced dramatic deterioration and structural damage to the spores and mycelium symbolized in wrinkled spores and deformed mycelia. Additionally, CNPs disrupted the dense configuration of biofilm indicated by reduction in biofilm mass and scattered distorted cells with rough pitted surfaces; reflecting the outflow of intracellular cytoplasmic components of the cells. Strikingly, Xia et al. [125] and Yang et al. [126] found such shriveled appearance that characterizes the cells of T. asahii treated with various NPs and antifungal agents.
Arguably, the polycationic nature of chitosan in nanoscale dimensions fostered not only their fungal inhibition performance, but also their capability in frustrating and destabilizing the biofilm formation, wherein the profusion of the amino groups (NH^3+^) in N-acetylglucosamine units of chitosan structure empowered the electrostatic attraction with negatively charged functional groups distributed on T. asahii cell membranes [38]. Seemingly, the high surface area with metal-chelating trait and simultaneously higher penetration capacity of CNPs facilitated essential metals capturing from the surrounding milieu; thwarting by such manner the easy flow of metal ions to their corresponding active sites at the essential fungal biomolecules [4]. Conclusively, as inferred from SEM micrographs, CNPs possessed the capacity to deteriorate cell membrane integrity, uplift wall permeability, induce osmotic imbalances, which led to a higher leakage rate of intracellular constituents. Let alone their ability to impede polymeric mucilaginous material formation in the biofilm structure; thereby irreversibly destabilizing cellular attachment/ adhesion to biotic or abiotic substrates. Nevertheless, the fungistatic property was exhibited by CNPs rather than fungicidal as stated by Rabea et al. [127] and Goy et al. [128]. Such contradicted findings with other scholars could be attributed to differences in characteristic features of CNPs (e.g., morphology, dimensions, aggregation, surface charge, etc.), which are related to the synthesis method, nature of reducing agents, and also capping agents. However, the microbial physiology, types, and counts under different pH, exposure time, temperature, and ionic strength are also taken into consideration [4].
Conclusions
This study demonstrates the successful green synthesis of CNPs using the cell-free supernatant of Penicillium crustosum strain HNSAM-7, which acted as a natural crosslinking and stabilizing agent. The synthesized CNPs exhibited nanoscale size (average 26.19 nm), uniform morphology, and strong positive surface charge (+ 26.2 mV), confirming their stability and desirable physicochemical properties. Optimization using a FCCCD effectively maximized CNPs yield (10.71 mg/mL). The biosynthesized CNPs (250 and 500 µg/mL) showed potent antifungal activity against Trichosporon asahii, as confirmed by time–kill assays and microstructural analyses, highlighting their potential as biosafe coatings to mitigate fungal-induced biocorrosion of building materials. Overall, this work introduces a sustainable and environmentally friendly nanotechnological strategy with possible applications in preventing microbial corrosion.
Limitations of the study
Despite the valuable insights obtained from time–kill kinetic assessments and ultrastructural examinations, certain limitations should be recognized. The study evaluated the antifungal activity of CNPs in vitro using a single fungal strain, Trichosporon asahii. Although this model is appropriate to explore the antifungal mechanism of CNPs, it does not entirely reflect the complexity of real-world environmental conditions. In real-world environmental conditions, numerous factors including host interactions, the presence of mixed microbial communities, variations in nutrient availability, and the physical and chemical characteristics of surfaces may have a substantial impact on antifungal efficacy. Therefore, future studies under real-world environmental conditions are recommended to confirm and extend the present findings. Evaluation of coating durability, adhesion, and long-term performance under environmental conditions was beyond the scope of the current study and will be addressed in future research. Planned follow-up studies will simulate realistic building material environments to validate the antifungal performance of CNPs coatings, ensuring transparency regarding the current study’s limitations.
Supplementary Information
Supplementary material 1.