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Record W3025001317

Mapping Longitudinal Inhomogeneity in Nanostructures Using Cross-Sectional Spatial Raman Imaging

2020· article· en· W3025001317 on OpenAlexaff
Manushree Tanwar, Devesh K. Pathak, Anjali Chaudhary, Priyanka Yogi, Shailendra K. Saxena, Rajesh Kumar

Bibliographic record

VenueThe Journal of Physical Chemistry · 2020
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicSpectroscopy Techniques in Biomedical and Chemical Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRaman spectroscopyNanostructureChemistryNanowireMicroscopyNanotechnologyOpticsMaterials sciencePhysics
DOInot available

Abstract

fetched live from OpenAlex

The presence of structural inhomogeneity and physical phenomena therein taking place at the microscopic level is very difficult to identify simultaneously using a holistic technique. Raman microscopy has been developed here and established for this purpose; it has been shown to successfully work on n- and p-type silicon nanowires, a well-known system otherwise, prepared using a chemical technique. A Raman microscopic image not only shows the presence of inhomogeneity in the nanocrystallites’ size but also quantifies the size and its effect on microscopic quantum phenomena. The Raman image has been shown to be a good blend of microscopic and spectroscopic techniques.

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)0.000
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 score0.023
Threshold uncertainty score0.375

Codex and Gemma teacher scores by category

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 integrity0.0000.001
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 head0.023
GPT teacher head0.340
Teacher spread0.317 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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