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Record W4409490019 · doi:10.1021/acsaelm.4c02297

Perovskite Solar Cells: From Fabrication to Failure

2025· article· en· W4409490019 on OpenAlex
Renita M. D’Souza, Timothy L. Kelly

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Electronic Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFabricationPerovskite (structure)Materials scienceNanotechnologyEngineeringChemical engineeringMedicine

Abstract

fetched live from OpenAlex

Over the past decade, there have been tremendous advances in the design and fabrication of perovskite solar cells (PSCs). The unique optoelectronic properties of lead halide perovskites have resulted in solution-processed solar cells with excellent efficiencies, and significant advances have now been made in both lab-scale and large-area devices. As the technology moves toward commercialization, it is increasingly important to understand the stability of the devices under operationally relevant conditions. What is the projected lifetime of a perovskite-based module? Which environmental factors (e.g., humidity, heat, light) are most pernicious? What is the precise mechanism of device failure? To this end, both in situ and operando characterization techniques have proven to be useful methods and have provided important information about perovskite crystallization, degradation, and transformation processes. This spotlight article discusses recent advances in PSCs from both a device fabrication and device failure perspective, highlighting our research group’s contributions along the way.

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.

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)
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 score0.140
Threshold uncertainty score1.000

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)0.0010.001

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 head0.003
GPT teacher head0.192
Teacher spread0.189 · 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