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Record W2962478399 · doi:10.1109/tnano.2019.2927951

Ligand Exchange Functionalization of CIS Quantum Dots for CIS/ZnO Film Heterojunctions

2019· article· en· W2962478399 on OpenAlexaff
Yaxin Zheng, Bahareh Sadeghimakki, Jacob A.L. Brunning, Siva Sivoththaman

Bibliographic record

VenueIEEE Transactions on Nanotechnology · 2019
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsQuantum dotBifunctionalLigand (biochemistry)HeterojunctionNanotechnologyMaterials scienceSurface modificationInterfacingNanoparticleChemistryOptoelectronicsComputer scienceCatalysisOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Colloidal quantum dots (QD) are rapidly making their way into several optoelectronic applications. While Cd- and Pb-based QDs are promising for high-performance devices, toxicity remains a concern. In this regard, Cu-In-S (CIS) QDs provide an alternative option for scalable, commercial production. A low-temperature, high-throughput process was used to synthesize CIS QDs with 1-dodecanethiol (DDT) ligands. While QD synthesis with a DDT ligand is facile, there are drawbacks when it comes to device implementation and interfacing with electron transport films such as ZnO. A ligand exchange process was employed to replace the DDT ligands in the as-synthesized QDs with 3-mercaptopropionic acid (MPA); owing to the short and bifunctional nature of the MPA molecules, this process improved the surface adhesion and carrier transport to ZnO. I-V measurements on planar structures showed the significance of ligand exchange in CIS QDs for heterojunction device implementation.

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 score0.003

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)0.0010.000

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 head0.022
GPT teacher head0.234
Teacher spread0.212 · 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

Citations7
Published2019
Admission routes1
Has abstractyes

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