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Record W1931781316 · doi:10.1039/c5cp02173a

Linking surface chemistry to optical properties of semiconductor nanocrystals

2015· article· en· W1931781316 on OpenAlex
Michael Krause, Patanjali Kambhampati

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsMcGill University
Fundersnot available
KeywordsNanocrystalSemiconductorNanotechnologySurface (topology)Semiconductor materialsChemistryMaterials scienceOptoelectronics

Abstract

fetched live from OpenAlex

The intricate chemistry occurring at the surface of semiconductor nanocrystals is crucial to tailoring their optical properties to a myriad of applications. This perspective aims to re-evaluate long held ideas in semiconductor nanocrystal surface science in the light of a body of new and rich research. We start by reviewing recent developments in ligand chemistry, followed by a discussion of spectroscopic and computational approaches used for advancing the poorly-understood electronic structure of the surface. With the insights gained, we show how the surface impacts emissive behaviour and we summarize strategies to increase fluorescent quantum yield. This discussion is followed by a review of experimental approaches for quantitative analysis of the surface chemistry at concentrations relevant to spectroscopic measurements. We end by highlighting some new directions in ligand chemistry, namely all-inorganically passivated semiconductor nanocrystals and new applications of surface emission.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.052
GPT teacher head0.251
Teacher spread0.199 · 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