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Record W4319740046 · doi:10.1063/5.0130727

Accelerating ultrafast processes in hydrogen-bonded complexes under pressure

2023· article· en· W4319740046 on OpenAlexaff
Qi Li, Lixia Zhu, Yu Wan, Yongfeng Wan, Jianbo Gao, Hang Yin, Ying Shi

Bibliographic record

VenueApplied Physics Letters · 2023
Typearticle
Languageen
FieldChemistry
TopicPhotochemistry and Electron Transfer Studies
Canadian institutionsBrock University
FundersNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsIntermolecular forceUltrafast laser spectroscopyEnergy conversion efficiencyUltrashort pulseHydrogenMethanolFemtosecondMaterials scienceChemical physicsSpectroscopyPhotochemistryChemistryOptoelectronicsOrganic chemistryOpticsMoleculeLaser

Abstract

fetched live from OpenAlex

Acceleration of ultrafast processes is vital in hydrogen-bonded coumarin–methanol complexes for improving the photoelectric conversion efficiency of dye-sensitized solar cells (DSSCs). The traditional methods expedite ultrafast processes individually related to electron injection in DSSCs, namely, internal conversion (IC) or intermolecular charge transfer (inter-CT), by adjusting molecular topologies. We introduce pressure as an external drive to realize the acceleration of both processes simultaneously without changing the configuration. In the definite hydrogen-bonded complexes, the acceleration of IC and inter-CT processes is visualized by in situ high-pressure femtosecond transient absorption spectroscopy. In liquid-phase methanol, the IC and inter-CT processes are actuated effectively from 150.20 to 59.21 fs and 93.95 to 29.05 ps, respectively. The quickening of both processes is attributed to the enhancement of intermolecular hydrogen bonds under pressure. After the pressure-induced methanol phase transition, the rates of IC and inter-CT processes at 3.67 GPa are increased by 36.42% and 80.55% compared to at 1.00 atm. Our study results open an enlightening avenue for boosting the photoelectric conversion efficiency of DSSCs.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
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 head0.024
GPT teacher head0.241
Teacher spread0.217 · 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
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

Citations33
Published2023
Admission routes1
Has abstractyes

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