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Record W2553106387 · doi:10.1038/srep37654

Thermodynamic origin of instability in hybrid halide perovskites

2016· article· en· W2553106387 on OpenAlexafffund
E. Tenuta, Chao Zheng, Oleg Rubel

Bibliographic record

VenueScientific Reports · 2016
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaCompute CanadaLakehead University
KeywordsPerovskite (structure)HalideInstabilitySolubilityDegradation (telecommunications)DecompositionMaterials scienceChemical stabilityAb initioSolar cellPerovskite solar cellChemical physicsChemistryThermodynamicsChemical engineeringPhysical chemistryInorganic chemistryComputer sciencePhysicsOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Abstract Degradation of hybrid halide perovskites under the influence of environmental factors impairs future prospects of using these materials as absorbers in solar cells. First principle calculations can be used as a guideline in search of new materials, provided we can rely on their predictive capabilities. We show that the instability of perovskites can be captured using ab initio total energy calculations for reactants and products augmented with additional thermodynamic data to account for finite temperature effects. Calculations suggest that the instability of CH3NH3PbI3 in moist environment is linked to the aqueous solubility of the CH3NH3I salt, thus making other perovskite materials with soluble decomposition products prone to degradation. Properties of NH3OHPbI3, NH3NH2PbI3, PH4PbI3, SbH4PbI3, CsPbBr3, and a new hypothetical SF3PbI3 perovskite are studied in the search for alternative solar cell absorber materials with enhanced chemical stability.

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: Simulation or modeling · Consensus signal: none
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 studies0.0000.001
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.226
Teacher spread0.215 · 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 designSimulation or modeling
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

Citations102
Published2016
Admission routes2
Has abstractyes

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