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Formation of Black Droplets from Coal Biosolubilization

2012· article· en· W2063582037 on OpenAlexaff
Kai Shi, Xiu Xiang Tao, Li Zhi, De Shun Kong, Shulong Yin

Bibliographic record

VenueAdvanced materials research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAdsorption and biosorption for pollutant removal
Canadian institutionsUniversity of Calgary
FundersNational Natural Science Foundation of China
KeywordsCoalCarbon blackChemical engineeringWater contentMaterials scienceMoistureChemistryComposite materialGeologyOrganic chemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

To study mechanisms of coal biosolubilization, this paper investigated the formation of black droplets on the surface of Sabouraud maltose agar (SMA) culture media through coal solubilisation by Hypocrea lixii AH. The black droplets were collected and weighted. Mycelia of Hypocrea lixii AH were characterized by (Full name is required before abbreviation) FTIR spectra and contact angles. The results showed that the first 9 days are black droplets permeation period, solubilized coal in coal pole keep place due to increase of polarity with mycelia aging. Moisture content of the black droplets was 96.4(±0.2) %, largely resulting in high-polarity property of the black droplets. Furthermore, the comparison on moisture contents of coal and black droplets indicated that the water content of black droplets were from the culture media and bio-oxidation instead of coal self.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.020
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.002

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 head0.048
GPT teacher head0.334
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

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
Published2012
Admission routes1
Has abstractyes

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