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Record W2041395494 · doi:10.1021/ja411843k

Reengineering the Optical Absorption Cross-Section of Photosynthetic Reaction Centers

2014· article· en· W2041395494 on OpenAlexafffund
Palash Dutta, Su Lin, Andrey Loskutov, Symon Levenberg, Daniel Jun, Rafael G. Saer, J. Thomas Beatty, Yan Liu, Hao Yan, Neal W. Woodbury

Bibliographic record

VenueJournal of the American Chemical Society · 2014
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPhotosynthetic Processes and Mechanisms
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaArmy Research OfficeMultidisciplinary University Research Initiative
KeywordsAlexa FluorChemistryAbsorbancePhotosynthetic reaction centrePhotochemistryRhodobacter sphaeroidesFluorescenceAbsorption (acoustics)Ultrafast laser spectroscopyAbsorption cross sectionAbsorption spectroscopyMoleculeSpectroscopyElectron transferAnalytical Chemistry (journal)PhotosynthesisCross section (physics)OpticsOrganic chemistry

Abstract

fetched live from OpenAlex

Engineered cysteine residues near the primary electron donor (P) of the reaction center from the purple photosynthetic bacterium Rhodobacter sphaeroides were covalently conjugated to each of several dye molecules in order to explore the geometric design and spectral requirements for energy transfer between an artificial antenna system and the reaction center. An average of 2.5 fluorescent dye molecules were attached at specific locations near P. The enhanced absorbance cross-section afforded by conjugation of Alexa Fluor 660 dyes resulted in a 2.2-fold increase in the formation of reaction center charge-separated state upon intensity-limited excitation at 650 nm. The effective increase in absorbance cross-section resulting from the conjugation of two other dyes, Alexa Fluor 647 and Alexa Fluor 750, was also investigated. The key parameters that dictate the efficiency of dye-to-reaction center energy transfer and subsequent charge separation were examined using both steady-state and time-resolved fluorescence spectroscopy as well as transient absorbance spectroscopy techniques. An understanding of these parameters is an important first step toward developing more complex model light-harvesting systems integrated with reaction centers.

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 score0.009
Threshold uncertainty score0.209

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 head0.005
GPT teacher head0.235
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.

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

Citations66
Published2014
Admission routes2
Has abstractyes

Explore more

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