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Record W3019190712 · doi:10.1002/adfm.202001692

Low‐Dimensional Contact Layers for Enhanced Perovskite Photodiodes

2020· article· en· W3019190712 on OpenAlexafffund
Deying Luo, Taoyu Zou, Wengqiang Yang, Beng Xiang, Xiaoyu Yang, Ya Wang, Rui Su, Lichen Zhao, Rui Zhu, Hang Zhou, Thomas P. Russell, Hongyu Yu, Zheng‐Hong Lu

Bibliographic record

VenueAdvanced Functional Materials · 2020
Typearticle
Languageen
FieldEngineering
TopicPerovskite Materials and Applications
Canadian institutionsUniversity of Toronto
FundersLawrence Berkeley National LaboratoryBasic Energy SciencesOffice of ScienceSouthern University of Science and TechnologyNatural Sciences and Engineering Research Council of CanadaOffice of Naval ResearchNational Natural Science Foundation of ChinaU.S. Department of Energy
KeywordsPhotodiodeMaterials sciencePerovskite (structure)OptoelectronicsPhotodetectorElectrodeCrystallography

Abstract

fetched live from OpenAlex

Abstract Controlling defects and energy‐band alignments are of paramount importance to the development of high‐performance perovskite‐based photodiodes. Yet, concurrent improvements in interfacial contacts and defect reduction simply by tailoring bottom contacts have not been investigated. An effective strategy is reported that can simultaneously improve energy‐band alignments and structural defects by introducing low‐dimensional contact (LDC) layers at the bottom interface. It is found that LDC‐based perovskites considerably suppress undesirable structural defects induced by microstrains, resulting in reduced nonradiative recombination centers and improved carrier lifetimes. Additionally, the resulting LDC‐based interface structures help block minority carrier injection from the electrodes by forming built‐in electric fields. As a consequence, LDC‐based perovskite photodiodes showed improved light detection capabilities. The result opens an avenue to yield highly efficient photodiodes.

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 categoriesInsufficient payload (model declined to judge)
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 score0.023
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)0.0010.000

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 head0.014
GPT teacher head0.214
Teacher spread0.200 · 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.

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

Citations40
Published2020
Admission routes2
Has abstractyes

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