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Record W3191131153 · doi:10.1364/oe.432995

Spectrally-tunable femtosecond single-molecule pump-probe spectroscopy

2021· article· en· W3191131153 on OpenAlexfundno aff
Raymundo Moya, Torû Kondô, Audrey C. Norris, Gabriela S. Schlau‐Cohen

Bibliographic record

VenueOptics Express · 2021
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced Fluorescence Microscopy Techniques
Canadian institutionsnot available
FundersNational Institute of General Medical SciencesCanadian Institute for Advanced ResearchRichard and Susan Smith Family FoundationArnold and Mabel Beckman FoundationAlfred P. Sloan Foundation
KeywordsFemtosecondDephasingSpectroscopyPicosecondRelaxation (psychology)Materials scienceMoleculeFluorescence spectroscopyMillisecondMolecular physicsFluorescence correlation spectroscopyNanosecondFluorescenceOpticsChemistryLaserPhysics

Abstract

fetched live from OpenAlex

Single-molecule spectroscopy has been extensively used to investigate heterogeneity in static and dynamic behaviors on millisecond and second timescales. More recently, single-molecule pump-probe spectroscopy emerged as a method to access heterogeneity on the femtosecond and picosecond timescales. Here, we develop a single-molecule pump-probe apparatus that is easily tunable across the visible region and demonstrate its utility on the widely-used fluorescent dye, Atto647N. A spectrally-independent, bimodal distribution of energetic relaxation time constants is found, where one peak corresponds to electronic dephasing (∼ 100 fs) and the other to intravibrational relaxation (∼ 300 fs). The bimodal nature indicates that relaxation within each individual molecule is dominated by only one of these processes. Both peaks of the distribution are narrow, suggesting little heterogeneity is present for either process. As illustrated here, spectrally-tunable single-molecule pump-probe spectroscopy will enable investigation of the heterogeneity in a wide range of biological and material systems.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.180
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.264
Teacher spread0.254 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreMethods

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

Citations14
Published2021
Admission routes1
Has abstractyes

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