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Record W2165516026 · doi:10.1039/c5cp01355k

Role of thermal excitation in ultrafast energy transfer in chlorosomes revealed by two-dimensional electronic spectroscopy

2015· article· en· W2165516026 on OpenAlexaff
Sunhong Jun, Cheolhee Yang, Tae Wu Kim, Megumi Isaji, Hitoshi Tamiaki, Hyotcherl Ihee, Jeongho Kim

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsKootenay Association for Science & Technology
Fundersnot available
KeywordsExcitationSpectroscopyUltrashort pulseEnergy transferChlorosomeThermalElectron spectroscopyMaterials scienceChemical physicsChemistryAtomic physicsPhysicsOpticsThermodynamicsLaserQuantum mechanicsOrganic chemistry

Abstract

fetched live from OpenAlex

Chlorosomes are the largest light harvesting complexes in nature and consist of many bacteriochlorophyll pigments forming self-assembled J-aggregates. In this work, we use two-dimensional electronic spectroscopy (2D-ES) to investigate ultrafast dynamics of excitation energy transfer (EET) in chlorosomes and their temperature dependence. From time evolution of the measured 2D electronic spectra of chlorosomes, we directly map out the distribution of the EET rate among the manifold of exciton states in a 2D energy space. In particular, it is found that the EET rate varies gradually depending on the energies of energy-donor and energy-acceptor states. In addition, from comparative 2D-ES measurements at 77 K and room temperature, we show that the EET rate exhibits subtle dependence on both the exciton energy and temperature, demonstrating the effect of thermal excitation on the EET rate. This observation suggests that active thermal excitation at room temperature prevents the excitation trapping at low-energy states and thus promotes efficient exciton diffusion in chlorosomes at ambient temperature.

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 score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.236
Teacher spread0.230 · 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

Citations15
Published2015
Admission routes1
Has abstractyes

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