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Record W4379113520 · doi:10.1021/acsanm.3c01772

Core–Shell Structured Fe<sub>3</sub>O<sub>4</sub>@CuS for Effective Gold Capture and Recovery

2023· article· en· W4379113520 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

VenueACS Applied Nano Materials · 2023
Typearticle
Languageen
FieldEngineering
TopicExtraction and Separation Processes
Canadian institutionsQueen's University
FundersQueen's University
KeywordsAdsorptionSelectivityMaterials scienceIon exchangeChemical engineeringIonNanotechnologyChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Gold is a valuable commodity with numerous industrial and technological applications. Traditional adsorbents for gold adsorption using activated carbon or ion exchange adsorption resin are limited by low capacity and selectivity. To overcome these limitations, this study introduces a core–shell structured magnetic adsorbent, Fe 3 O 4 @CuS, which can capture and recover gold through in situ reduction. The resulting gold-loaded adsorbent can be easily separated using an external magnet, offering significant advantages such as high efficiency, ease of operation, low cost, low materials consumption, and low waste generation. Results show that this adsorbent has a gold loading capacity of 407 mg/g within 4 h, and the maximum capacity can reach up to 558.7 mg/g. Moreover, this magnetic adsorbent exhibited good selectivity for gold in the presence of other base metals. This research shows the feasibility of using Fe 3 O 4 @CuS for efficient gold recovery and provides insights into its adsorption behavior and mechanism.

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)0.000
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 score0.012
Threshold uncertainty score1.000

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 integrity0.0000.000
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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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