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Record W1594586338

Optical spectroscopy of photosynthetic complexes : focus on low-temperature protein dynamics

2010· dissertation· en· W1594586338 on OpenAlexfundno aff
Nicoleta Herascu

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2010
Typedissertation
Languageen
FieldPhysics and Astronomy
TopicSpectroscopy and Quantum Chemical Studies
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSpectroscopyChemical physicsProtein dynamicsContext (archaeology)PhotosynthesisFluorescence spectroscopyChemistrySpectral hole burningLight-harvesting complexSpectral resolutionDiffusionBiological systemMolecular dynamicsPhysicsPhotosystem IISpectral lineFluorescenceComputational chemistryOpticsBiologyThermodynamics
DOInot available

Abstract

fetched live from OpenAlex

To perform photosynthesis, plants, algae and bacteria possess well organized and closely coupled photosynthetic pigment-protein complexes. The information on energy transfer processes and protein dynamics contained in the narrow zero-phonon lines at low temperatures is hidden under the inhomogeneous broadening. Thus, it is difficult to analyze the spectroscopic properties of these complexes in sufficient detail by conventional spectroscopy methods. In this context, high resolution spectroscopy techniques such as Spectral Hole Burning, Fluorescence Line Narrowing and Single Molecule / Single Complex Spectroscopy are powerful tools designed to overcome the inhomogeneous broadening difficulty. This thesis focuses mainly on the low-temperature protein dynamics of several photosynthetic protein complexes (LH2, CP43, CP29 and LHCII). The hole growth kinetics and the shape of the anti-hole due to the non-photochemical spectral hole burning have been explored, and interpreted within the framework of theoretical models describing spectral diffusion due to conformational changes between nearly identical substates on a multi-tier protein energy landscapes.

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), Research integrity
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 score0.193
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.003
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 head0.011
GPT teacher head0.269
Teacher spread0.258 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

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