# Roblonski: A Material-Efficient Robo-Fluidic Toolbox for Rapid Photochemical Characterization

## TL;DR

Roblonski is a microfluidic robotic platform that automates and streamlines photochemical characterization with high precision and reduced material use.

## Contribution

The novel contribution is a compact, material-efficient robotic platform for automated photochemical assays with high precision and reproducibility.

## Key findings

- Roblonski's results matched manual measurements and literature benchmarks across various conditions.
- The platform reduces sample consumption by 20-fold in volume and 1000-fold in reagent moles.
- It accelerates data collection by fourfold compared to traditional methods.

## Abstract

Reliable photochemical
and photophysical characterization
is essential
for understanding and optimizing photocatalytic processes; however,
traditional, manual spectroscopic methods for determining bimolecular
photoreaction quenching constants, molar extinction coefficients,
and photoluminescence quantum yields (PLQYs) are time-, cost-, material-,
and labor-intensive and generate considerable chemical waste. Herein,
we report Roblonski, a compact, material-efficient microfluidic robotic
platform that automates these three foundational photochemical assays
with high precision, reproducibility, and accuracy. Using Ru­(bpy)3(PF6)2 as a model photosensitizer and
photocatalyst, we performed Stern–Volmer analyses with 11 excited
state electron and triplet energy transfer quenchers, Beer–Lambert
studies of five compounds spanning 3 orders of magnitude in their
molar extinction coefficients across multiple solvents, and relative
PLQY determinations for fluorophores/luminophores with PLQYs ranging
over 3 orders of magnitude in efficiency. The machine-generated results
matched manual experimental measurements and literature benchmarks
across diverse spectral features, solvent environments, and signal
intensity regimes. Roblonski reduces sample consumption (20-fold by
solution volume, 1000-fold by reagent moles) and accelerates data
collection (4-fold) compared to traditional, manual approaches. By
integrating these photochemically relevant assays into a single, compact
automated platform, Roblonski has the potential to lower experimental
barriers, enable data-rich evaluation of photocatalysts and substrates,
and augment autonomous photochemical discovery and characterization.