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Record W4285497552 · doi:10.54097/hset.v5i.745

The Recent Progress and the state-of-art applications of Perovskite Solar Cells

2022· article· en· W4285497552 on OpenAlexaff
Hongkun Li

Bibliographic record

VenueHighlights in Science Engineering and Technology · 2022
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPerovskite (structure)Energy conversion efficiencyMaterials scienceNanotechnologyEngineering physicsTandemStackingProcess engineeringChemistryEngineeringChemical engineeringOptoelectronicsComposite material

Abstract

fetched live from OpenAlex

With the awareness of using clean and sustainable energy, the utilization of solar power is of great importance in human society. Following the trend, solar cells are required to have higher and higher power conversion efficiency. Contemporarily, perovskite materials, as a new type of materials for construction of solar cells, exhibits great potential to have high efficiency. This article focuses on the methods on improving power conversion efficiencies of perovskite solar cells and discusses the limitation of recent technologies and industrial applications, and the future prospect of perovskite solar cells. To be specific, all the methods are focusing on the selection of materials suitable for cells design, from CsSnI3 to lead-based organic materials, the efficiencies have increased significantly. The method of stacking perovskite solar cells to make tandem solar cells improved efficiencies among all the methods. Meanwhile, the toxicity, low stability and difficulties in large-scale application are the main limitations for perovskite solar cells. For the future studies, it is important to search for materials with low toxicity and high stability. The technology for improving efficiency of large-scale solar cells is also required. These results provide a guideline for the future study in perovskite solar cells.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.280

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.003
GPT teacher head0.191
Teacher spread0.188 · 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.

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

Citations1
Published2022
Admission routes1
Has abstractyes

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