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Record W4409143795 · doi:10.1021/acsnano.5c03944

Coherent Multidimensional Spectroscopy Reveals Hot Exciton Cooling Landscapes in CsPbBr<sub>3</sub> Quantum Dots

2025· article· en· W4409143795 on OpenAlexafffund
Arnab Ghosh, Carlos Mora Perez, Patrick Brosseau, Dmitry N. Dirin, Oleg V. Prezhdo, Maksym V. Kovalenko, Patanjali Kambhampati

Bibliographic record

VenueACS Nano · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMcGill University
FundersH2020 European Research CouncilDivision of ChemistryNatural Sciences and Engineering Research Council of CanadaSony ElectronicsFonds Québécois de la Recherche sur la Nature et les TechnologiesCanada Foundation for Innovation
KeywordsQuantum dotSpectroscopyExcitonPhysicsMaterials scienceCoherent spectroscopyBiexcitonCondensed matter physicsMolecular physicsNanotechnologyQuantum mechanicsRaman spectroscopy

Abstract

fetched live from OpenAlex

Hot exciton relaxation dynamics is one of the main processes in quantum dots (QD), conferring their functions in optoelectronic devices spanning photovoltaics and solar fuel generation to light emitting diodes, lasers, and quantum light sources. The challenge has been to monitor energy relaxation dynamics in parallel with resolution of excitation or excess energy. Here, we exploit the unique capacity of Coherent Multi-Dimensional Spectroscopy (CMDS) to provide the first observation of the hot exciton cooling landscape of a large size range of CsPbBr 3 lead halide perovskite QD, notable for their impact on optoelectronic devices, as well as their strong and unique exciton-lattice coupling. The CMDS data reveal that the hot exciton relaxation landscape is a complex function of the energy. Ab initio quantum dynamics simulations rationalize the observed behavior through energy dependent nonadiabatic exciton–phonon coupling. This first observation of cooling landscapes in QD suggests that materials science that either accelerates or slows hot exciton cooling can better be understood as a landscape to optimize for applications.

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 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 score0.019
Threshold uncertainty score0.626

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 head0.007
GPT teacher head0.230
Teacher spread0.224 · 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.

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

Citations10
Published2025
Admission routes2
Has abstractyes

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