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Record W2407896445 · doi:10.1002/adfm.201601570

Graphdiyne: An Efficient Hole Transporter for Stable High‐Performance Colloidal Quantum Dot Solar Cells

2016· article· en· W2407896445 on OpenAlexaff
Zhiwen Jin, Mingjian Yuan, Hui Li, Hui Yang, Qing Zhou, Huibiao Liu, Xinzheng Lan, Mengxia Liu, Jizheng Wang, Edward H. Sargent, Yuliang Li

Bibliographic record

VenueAdvanced Functional Materials · 2016
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceQuantum dotAnodeOptoelectronicsWork functionEnergy conversion efficiencyPhotovoltaic systemColloidLayer (electronics)Active layerNanotechnologyElectrodeChemical engineeringPhysics

Abstract

fetched live from OpenAlex

Graphdiyne, a novel large π‐conjugated carbon hole transporting material, is employed as anode buffer layer in colloidal quantum dots solar cells. Power conversion efficiency is notably enhanced to 10.64% from 9.49% compared to relevant reference devices. Hole transfer from the quantum dot solid active layer to the anode can be appreciably enhanced only by using graphdiyne to lower the work function of the colloidal quantum dot solid. It is found that the all‐carbon buffer layer prolongs the carrier lifetime, reducing surface recombination on the previously neglected back side of the photovoltaic device. Remarkably, the device also shows high long‐term stability in ambient air. The results demonstrate that graphdiyne may have diverse applications in enhancing optoelectronic devices.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.017
GPT teacher head0.212
Teacher spread0.195 · 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 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

Citations194
Published2016
Admission routes1
Has abstractyes

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