MétaCan
Menu
Back to cohort
Record W3197182136 · doi:10.1088/2631-8695/ac2353

A review of primary technologies of thin-film solar cells

2021· review· en· W3197182136 on OpenAlexaff
Erteza Tawsif Efaz, M. M. Rhaman, Safayat-Al Imam, Khandaker Lubaba Bashar, Fahmid Kabir, MD Ehasan Mourtaza, Syed Nazmus Sakib, F. A. Mozahid

Bibliographic record

VenueEngineering Research Express · 2021
Typereview
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPrimary (astronomy)Materials scienceThin film solar cellEngineering physicsThin filmNanotechnologyEngineeringPhysicsAstronomy

Abstract

fetched live from OpenAlex

Abstract Thin-film solar cells are preferable for their cost-effective nature, least use of material, and an optimistic trend in the rise of efficiency. This paper presents a holistic review regarding 3 major types of thin-film solar cells including cadmium telluride (CdTe), copper indium gallium selenide (CIGS), and amorphous silicon ( α -Si) from their inception to the best laboratory-developed module. The remarkable evolution, cell configuration, limitations, cell performance, and global market share of each technology are discussed. The reliability, availability of cell materials, and comparison of different properties are equally explored for the corresponding technologies. The emerging solar cell technologies holding some key factors and solutions for future development are also mentioned. The summarized part of this comparative study is targeted to help the readers to decipher possible research scopes considering proper applications and productions of 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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.081
GPT teacher head0.335
Teacher spread0.254 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations134
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueEngineering Research ExpressSame topicChalcogenide Semiconductor Thin FilmsFrench-language works237,207