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Record W3204628253 · doi:10.17762/de.vi.5046

Bi-combustion synthesis technique for the reduction of crystallite size of nanophosphors - a modified version

2021· article· en· W3204628253 on OpenAlexvenueno aff
Shubhra Mishra D. S. Kshatri

Bibliographic record

VenueDesign Engineering · 2021
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence Properties of Advanced Materials
Canadian institutionsnot available
Fundersnot available
KeywordsCrystalliteHigh-resolution transmission electron microscopyMaterials scienceNanomaterialsNanocrystalline materialNanotechnologyNanoparticleTransmission electron microscopyDiffractionCombustionChemical engineeringComposite numberComposite materialOpticsChemistryPhysicsMetallurgyPhysical chemistry

Abstract

fetched live from OpenAlex

Nanophase materials, in recent times, have attracted many a researcher all over the world, on account of their exceptionally high efficiency in terms of morphological and optical behavior. In the nano-range order, various physical and chemical methods are employed to produce materials commercially, but the reported methods owing to their own physical conditions, limit the crystallite sizes to a certain nano-order. To prevail over this size-related limitation, a new modified bi-combustion synthesis technique (B-CST) has been introduced, which aids inthe formation of nanomaterials, with an average size of 10-20 nm, without using any ball milling process. In order to scrutinize the crystallite sizes of SrAl2O4: Eu2+, Dy3+ phosphors synthesized by CST and B-CST, the X-ray diffraction (XRD) technique is used to determine the crystalline phase only while high-resolution transmission electron microscopy (HRTEM) is used, which is the most sought-after method world-wide and is vigorously used to determine the crystallite size.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.018
GPT teacher head0.216
Teacher spread0.198 · 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
Published2021
Admission routes1
Has abstractyes

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