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Record W2773438126 · doi:10.1039/c7dt04343k

Prospects for efficient solar energy upconversion using metalloporphyrins as dual absorber-upconverters

2017· article· en· W2773438126 on OpenAlexafffund
Ronald P. Steer

Bibliographic record

VenueDalton Transactions · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPorphyrin and Phthalocyanine Chemistry
Canadian institutionsSaskatoon Medical ImagingUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhoton upconversionDual (grammatical number)Dual purposeMaterials scienceSolar energyOptoelectronicsSolar energy conversionPhotochemistryNanotechnologyChemistryDopingEngineeringElectrical engineeringMechanical engineering

Abstract

fetched live from OpenAlex

The novel potential use of selected metalloporphyrins as dual absorber-upconverters in solar photovoltaics is discussed. Additional efficiencies are available if use can be made of the porphyrin's short-lived S2 state, which is formed directly by excitation in the strong Soret transition in the blue-violet and also by absorption in the Q bands followed by rapid intersystem crossing and upconversion by triplet-triplet annihilation. The main challenge in realizing a working photovoltaic based on such a protocol is that energy must be extracted from the S2 state of the porphyrin within its picosecond lifetime. The structure-property relationships that may be used to select metalloporphyrins with the longest possible intrinsic lifetimes are outlined. The prospects for energy extraction from S2via ultrafast electron transfer or ultrafast resonant electronic energy transfer within a solid structure designed to maximize efficiency are discussed. Both MOF and pendant porphyrin polymer structures offer reasonable possibilities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.270
Teacher spread0.246 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2017
Admission routes2
Has abstractyes

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Same venueDalton TransactionsSame topicPorphyrin and Phthalocyanine ChemistryFrench-language works237,207