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Record W4417393104 · doi:10.1364/josab.580784

Investigation of non-radiative relaxation dynamics under pulsed excitation using photon absorption remote sensing: a proof-of-principle study in mechanical sensing

2025· article· en· W4417393104 on OpenAlexfundno aff
Channprit Kaur, Aria Hajiahmadi, Ben Ecclestone, James Tweel, J. E. Simmons, Parsin Haji Reza

Bibliographic record

VenueJournal of the Optical Society of America B · 2025
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsnot available
FundersCentre for Bioengineering and Biotechnology, University of WaterlooNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchUniversity of WaterlooCanada Foundation for Innovation
KeywordsAbsorption (acoustics)ExcitationScatteringPolystyreneThermalLight scatteringPhotonRelaxation (psychology)

Abstract

fetched live from OpenAlex

In this study, we present a non-radiative photon absorption remote sensing (NR-PARS) submodule as a method for mechanical sensing of single micro-objects. NR-PARS employs probe beam scattering to capture the non-radiative relaxation process following the absorption of a light pulse. When operated at a gigahertz-range bandwidth, NR-PARS resolves sub-nanosecond dynamics, tracing both photoacoustic (PA) pressure propagation and thermal diffusion. Coupled with a developed descriptive model, this GHz-range measurement enables retrieval of minimally distorted PA temporal profiles, which encode the ratio between the absorber’s sound speed and diameter. Proof-of-principle experiments with polystyrene microspheres demonstrate the ability to assess elastic properties at the single-particle level.

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 score0.001
Threshold uncertainty score0.002

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 communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.268
Teacher spread0.252 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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