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Record W4389742589 · doi:10.1117/1.jpe.13.042301

Status report on emerging photovoltaics

2023· article· en· W4389742589 on OpenAlexaff
Annick Anctil, Meghan N. Beattie, Christopher Case, Aditya Chaudhary, Benjamin D. Chrysler, Michael G. Debije, Stephanie Essig, D. K. Ferry, Vivian E. Ferry, Marina Freitag, Isaac E. Gould, Karin Hinzer, Harald Hoppe, Olle Inganäs, Lethy Krishnan Jagadamma, Min Hun Jee, Raymond K. Kostuk, Daniel Kirk, Stephan Kube, Min-Young Lim, Joseph M. Luther, Lorelle M. Mansfield, Michael D. McGehee, Duong Nguyen Minh, Preeti Nain, Matthew O. Reese, Angèle Reinders, Ifor D. W. Samuel, Wilfried van Sark, Hele Savin, Ian R. Sellers, Sean E. Shaheen, Zheng Tang, Fatima Toor, Ville Vähänissi, Ella Wassweiler, Emily L. Warren, Vincent R. Whiteside, Han Young Woo, Gang Xiong, Xitong Zhu

Bibliographic record

VenueJournal of Photonics for Energy · 2023
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsUniversity of Ottawa
FundersNational Renewable Energy LaboratoryDivision of Chemical, Bioengineering, Environmental, and Transport SystemsAalto-YliopistoNational Research FoundationEngineering and Physical Sciences Research CouncilU.S. Department of EnergyNational Science Foundation
KeywordsCommercializationPhotovoltaic systemPhotovoltaicsSustainabilityEmerging technologiesNanotechnologyEngineering physicsEnvironmental scienceMaterials scienceBusinessEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This report provides a snapshot of emerging photovoltaic (PV) technologies. It consists of concise contributions from experts in a wide range of fields including silicon, thin film, III-V, perovskite, organic, and dye-sensitized PVs. Strategies for exceeding the detailed balance limit and for light managing are presented, followed by a section detailing key applications and commercialization pathways. A section on sustainability then discusses the need for minimization of the environmental footprint in PV manufacturing and recycling. The report concludes with a perspective based on broad survey questions presented to the contributing authors regarding the needs and future evolution of PV.

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)0.003
metaresearch head score (Gemma)0.003
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: Not applicable
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.167

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0500.022

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 head0.247
Teacher spread0.234 · 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

Citations22
Published2023
Admission routes1
Has abstractyes

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