MétaCan
Menu
Back to cohort
Record W2805500960 · doi:10.1139/cjc-2018-0165

Systematic evaluation of inorganic salts as a heat sink for the magnesiothermic reduction of silica

2018· article· en· W2805500960 on OpenAlexaffvenue
Logesh Khanna, Yiqi Lai, Mita Dasog

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsDalhousie University
Fundersnot available
KeywordsChemistryPotassiumSodiumChlorideInorganic chemistryCrystalliteAdsorptionChemical engineeringSiliconPotassium bromideScanning electron microscopeNuclear chemistryOrganic chemistryMaterials science

Abstract

fetched live from OpenAlex

In this study, the effectivity of a series of inorganic salts, sodium chloride, calcium chloride, magnesium chloride, potassium chloride, and sodium bromide as heat sinks during magnesiothermic reduction of silica to porous silicon was investigated. The salts were chosen based on cost, thermal stability, ability to remain chemically inert during the reduction process, and ease of removal after the reaction. The structural integrity of the spherical porous silicon nanoparticles was observed using scanning electron microscopy, the surface area was determined via nitrogen adsorption experiments, and the crystallite size was determined using powder X-ray diffraction analysis; together, these were used to determine the efficacy of each salt. The ability of a salt to act as an effective heat sink was found to be highly correlated and principally dependent on the heat capacity of the salt. Calcium chloride was found to be the most effective heat sink overall among the five heat sinks investigated here.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.953

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.013
GPT teacher head0.255
Teacher spread0.242 · 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

Citations8
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207