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Record W2786368303 · doi:10.1149/08513.0543ecst

Organic-Inorganic Nanohybrid Materials for Photovoltaic Applications

2018· article· en· W2786368303 on OpenAlexafffund
Alexander E. Kobryn, Sergey Gusarov, Karthik Shankar

Bibliographic record

VenueECS Transactions · 2018
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridNational Research Council CanadaCompute Canada
KeywordsSolvationMaterials scienceChemical physicsOrganic solar cellNanoscopic scaleAdsorptionHeterojunctionPhotovoltaic systemPhotovoltaicsSoft matterNanotechnologyAcceptorMoleculeChemical engineeringChemistryPhysical chemistryPolymerOrganic chemistryPhysicsOptoelectronicsComposite materialColloid

Abstract

fetched live from OpenAlex

Thin film morphology is a key factor determining the performance of bulk heterojunction organic-inorganic solar cells through its influence on charge separation, charge transport and recombination losses in donor-acceptor blends. With this respect, both descriptive and predictive modeling of structural properties of blends of organic-inorganic perovskites of the type CH3NH3PbX3 (X=Cl, Br, or I) with P3HT or P3BT, including adsorption on TiO2 clusters having rutile (110) surface, is presented with the use of a methodo-logy that allows computing the microscopic structure of blends on the nanometer scale. The methodology is based on the integral equation theory of molecular liquids in the reference interaction site model and uses the universal force field. It provides a detailed microscopic insight into the organization of solvent molecules in the solvation shell structure and their contribution to the solvation thermodynamics. The calculated nanoscale morphologies serve as an instrument in rational design of hybrid photovoltaics.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.592
Threshold uncertainty score0.997

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)0.0040.001

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 head0.012
GPT teacher head0.220
Teacher spread0.209 · 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

Citations1
Published2018
Admission routes2
Has abstractyes

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