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Record W3115796650 · doi:10.21203/rs.3.rs-120373/v1

Controlling potential landscape of heterostructured nanocrystals with interfacial polarization

2020· preprint· en· W3115796650 on OpenAlexaff
Byeong Guk Jeong, Jun Hyuk Chang, Donghyo Hahm, Seunghyun Rhee, Myeongjin Park, Sohoo Lee, Youngdu Kim, Doyoon Shin, Jeong Woo Park, Changhee Lee, Doh C. Lee, Kyoungwon Park, E. H. Hwang, Wan Ki Bae

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsKootenay Association for Science & Technology
FundersSamsungNational Research Foundation of KoreaMinistry of Trade, Industry and EnergyNational Research Foundation
KeywordsNanocrystalPolarization (electrochemistry)Materials scienceNanotechnologyOptoelectronicsChemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Abstract The potential profile and the energy level offset of core/shell heterostructured nanocrystals (h-NCs) determine the photophysical properties and the charge transport characteristics of h-NC solids. However, limited material choices for heavy metal-free III-V/II-VI h-NCs pose challenges in comprehensive control of the potential profile. Herein, we present an unprecedented approach to such control by steering dipole moments at the interface of III-V/II-VI h-NCs. The controllable heterovalency at the interface is responsible for interfacial dipole moments that result in the vacuum-level shift, providing an additional knob for the control of optical and electrical characteristics of h-NCs. We capitalize on the atomic precision with which to synthesize h-NCs by correlating interfacial dipole moments to photochemical stability and optoelectronic performance of resulting h-NCs.

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)0.0010.000

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 head0.047
GPT teacher head0.318
Teacher spread0.271 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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