# Unraveling Charge Transfer Mechanisms in Graphene–Quantum Dot Hybrids for High-Sensitivity Biosensing

## TL;DR

This paper explores how charge transfer in graphene-quantum dot hybrids enables highly sensitive biosensing of biomarkers like IgG at very low concentrations.

## Contribution

The study introduces a quantum mechanical model to distinguish charge transfer from energy transfer and applies it to ultrasensitive biosensing.

## Key findings

- PL quenching and recovery in graphene–QD hybrids are driven by charge transfer, not energy transfer.
- A simple analytical model differentiates charge transfer from resonance energy transfer mechanisms.
- The system detects IgG at femtomolar concentrations using charge transfer-induced PL recovery.

## Abstract

Colloidal quantum dots (QDs) and graphene hybrids have emerged as promising platforms for optoelectronic and biosensing applications due to their unique photophysical and electronic properties. This study investigates the fundamental mechanism underlying the photoluminescence (PL) quenching and recovery in graphene–QD hybrid systems using single-layer graphene field-effect transistors (SLG-FETs) and time-resolved photoluminescence (TRPL) spectroscopy. We demonstrate that PL quenching and its recovery are primarily driven by charge transfer, as evidenced by an unchanged fluorescence lifetime upon quenching. Density functional theory calculations reveal a significant charge redistribution at the graphene–QD interface, corroborating experimental observations. We also provide a simple analytical quantum mechanical model to differentiate charge transfer-induced PL quenching from resonance energy transfer. Furthermore, we leverage the charge transfer mechanism for ultrasensitive biosensing to detect biomarkers such as immunoglobulin G (IgG) at femtomolar concentrations. The sensor’s electrical response, characterized by systematic shifts in the Dirac point of SLG-FETs, confirms the role of analyte-induced charge modulation in PL recovery. Our findings provide a fundamental framework for designing next-generation graphene-based biosensors with exceptional sensitivity and specificity.