Quick Navigation

Topics

Quantum Machine Learning Spin Qubits Silicon Quantum Computing Quantum Chemistry

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

PubMed
Authors: Arshad A, Canty RB, Danilov EO, Abolhasani M, Castellano FN

Year

2026

Paper ID

45120

Status

Peer-reviewed

Abstract Read

~2 min

Abstract Words

198

Citations

1

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)(PF) 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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2026 reference point for readers tracking recent quantum research.
  • Reliable photochemical and photophysical characterization is essential for understanding and optimizing photocatalytic processes; however, traditional, manual spectroscopic...

Paper Tools

Become a member to use research tools

Sign in to open papers, visit source links, share, cite, compare, copy DOI links, request category corrections, and build your reading list.

Publisher Share Cite This Paper Copy URL Compare Copy DOI Add to Reading List Category Correction Request

References & Citation Signals

Local Citation Graph (Related-Paper Links)

Current Paper #45120 #78837 TLS loss in superconducting res... #78828 Verifiable Sampling for Delegat... #78813 Large-Scale Benchmarking of Qua... #78797 Local polarization control of o...

External citation index: OpenAlex citation signal • updated 2026-10-25 15:28:40

Community Reactions

Quick sentiment from readers on this paper.

Score: 0
Likes: 0 Dislikes: 0

Sign in to react to this paper.

Discussion & Reviews (Moderated)

Average Rating: 0.0 / 5 (0 ratings)

No written reviews yet.