MétaCan
Menu
Back to cohort
Record W3036936269 · doi:10.1002/advs.202000894

Colloidal Quantum Dot Bulk Heterojunction Solids with Near‐Unity Charge Extraction Efficiency

2020· article· en· W3036936269 on OpenAlexafffund
Min‐Jae Choi, Se‐Woong Baek, Seungjin Lee, Margherita Biondi, Chao Zheng, Petar Todorović́, Peicheng Li, Sjoerd Hoogland, Zheng‐Hong Lu, F. Pelayo Garcı́a de Arquer, Edward H. Sargent

Bibliographic record

VenueAdvanced Science · 2020
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOptoelectronicsDopingQuantum dotHeterojunctionCharge carrierBand gapAttenuation coefficientAbsorption (acoustics)Band offsetInfraredNanotechnologyOptics

Abstract

fetched live from OpenAlex

Abstract Colloidal quantum dots (CQDs) are of interest for optoelectronic applications owing to their tunable properties and ease of processing. Large‐diameter CQDs offer optical response in the infrared (IR), beyond the bandgap of c‐Si and perovskites. The absorption coefficient of IR CQDs (≈10 4 cm −1 ) entails the need for micrometer‐thick films to maximize the absorption of IR light. This exceeds the thickness compatible with the efficient extraction of photogenerated carriers, a fact that limits device performance. Here, CQD bulk heterojunction solids are demonstrated that, with extended carrier transport length, enable efficient IR light harvesting. An in‐solution doping strategy for large‐diameter CQDs is devised that addresses the complex interplay between (100) facets and doping agents, enabling to control CQD doping, energetic configuration, and size homogeneity. The hetero‐offset between n ‐type CQDs and p ‐type CQDs is manipulated to drive the transfer of electrons and holes into distinct carrier extraction pathways. This enables to form active layers exceeding thicknesses of 700 nm without compromising open‐circuit voltage and fill factor. As a result, >90% charge extraction efficiency across the ultraviolet to IR range (350–1400 nm) is documented.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.026
GPT teacher head0.261
Teacher spread0.235 · 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

Citations45
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced ScienceSame topicQuantum Dots Synthesis And PropertiesFrench-language works237,207