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Record W3217425401 · doi:10.33774/chemrxiv-2021-b36vm

Ultrafast reclamation of fracking effluents using surface-engineered nanosilicon sponges

2021· preprint· en· W3217425401 on OpenAlex

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnergy
TopicSolar-Powered Water Purification Methods
Canadian institutionsUniversity of Toronto
FundersFisheries and Oceans CanadaCanada Foundation for InnovationNational Natural Science Foundation of ChinaImperial College LondonUniversity of TorontoSuncor Energy Incorporated
KeywordsLand reclamationEffluentDiluentEnvironmental scienceWaste managementWastewaterPulp and paper industryChemistryEnvironmental engineeringEngineeringNuclear chemistry

Abstract

fetched live from OpenAlex

Effluents from the fracking process are typically discharged at elevated temperatures and are a major environmental concern. We applied a surface-engineered sponge (SEnS) with thermal stability up to 220 ºC to reclaim emulsified oily wastewater at discharge temperatures between 30-100 ºC. The sponge achieved 92-96% removal efficiency within 5 minutes, where speed increased by 27% by melting waxes. The adsorbed oil from the SEnS was also recovered within 1-2 minutes by diluent wash. These performance metrics suggest that SEnS could emerge as a practical solution to achieve fracking water reclamation processes’ Net-Zero goals.

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)0.001
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 score0.289
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
Research integrity0.0010.001
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 head0.059
GPT teacher head0.319
Teacher spread0.260 · 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

Quick stats

Citations0
Published2021
Admission routes2
Has abstractyes

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