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Record W4413897370 · doi:10.26434/chemrxiv-2025-cjbf3

Impact of Solvation on the Photoisomerization Mechanism of Oxindole Switches with Electron-Donating Substituents

2025· article· en· W4413897370 on OpenAlexaff
Miquel Huix‐Rotllant, Abdul Qadeer, Daniel Roca‐Sanjuán, Javier Segarra‐Martí, Cheol Ho Choi

Bibliographic record

VenueChemRxiv · 2025
Typearticle
Languageen
FieldMaterials Science
TopicPhotochromic and Fluorescence Chemistry
Canadian institutionsCanadian Nautical Research Society
FundersMinistry of Science and ICT, South KoreaNational Research Foundation of KoreaAgence Nationale de la RechercheNational Research Foundation
KeywordsPhotoisomerizationSolvationOxindoleMechanism (biology)ChemistryPhotochemistryElectronComputational chemistryIsomerizationOrganic chemistryPhysicsCatalysisSolventQuantum mechanics

Abstract

fetched live from OpenAlex

Molecular photoswitches that undergo Z/E photoisomerization are fundamental components of light-driven molecular machines. Their efficiency is determined by ultrafast isomerization dynamics and high quantum yields, both of which are strongly modulated by the solvent environment. In this work, we examine two protonation states of an oxindole-based photoswitch in methanol. Using a combination of quantum mechanics/molecular mechanics (QM/MM) simulations with semi-empirical and high-level electronic structure methods, we compute radial distribution functions, isomerization free energy barriers, absorption spectra, and nonadiabatic dynamics in solution. We find that the deprotonated oxindole establishes stronger hydrogen bonds with methanol, whereas the protonated form adopts a pre-twisted conformation in solution. Nonadiabatic dynamics reveal that the weaker hydrogen bonding of the protonated state permits coherent isomerization, in contrast to the deprotonated form, where solvent fluctuations exert a stronger influence on individual trajectories. These findings clarify how solvent interactions govern the photoisomerization pathways of molecular photoswitches and inform the design of light-driven molecular machines, thereby enabling precise control of their operation in the condensed phase.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.248
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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