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Record W2984617426 · doi:10.1002/cssc.201902955

Natural Kaolin: Sustainable Technology for the Instantaneous and Energy‐Neutral Recycling of Anthropogenic Mercury Emissions

2019· article· en· W2984617426 on OpenAlexafffund
Mainak Ganguly, Yuanyuan Tao, Bryan Lee, Parisa A. Ariya

Bibliographic record

VenueChemSusChem · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsMcGill University
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMercury (programming language)Environmental scienceSustainable energyEnvironmental chemistryNatural (archaeology)Waste managementChemistryRenewable energyEngineeringGeology

Abstract

fetched live from OpenAlex

Abstract Kaolin, a natural and inexpensive clay mineral, is ubiquitous in soil, dirt, and airborne particles. Amongst four commonly available clay minerals, kaolin, as a result of its layered structure, is the most efficient natural gaseous Hg adsorbent to date (Langmuir maximum adsorption capacity Q m =574.08 μg g −1 and Freundlich Q m =756.49 μg g −1 ). The Hg uptake proceeds by homogeneous monolayer and heterogeneous processes. Hg physisorption on kaolin occurs in the dark, yet the adsorption rate is enhanced upon irradiation. The effects of several metal complexes, salts, halides and solvents on the Hg uptake were examined. The addition of CuCl 2 particles leads to a significant enhancement of the Hg uptake capacity (>30 times) within second timescales and without irradiation. The physisorption with kaolin is switched to chemisorption upon the addition of CuCl 2 to kaolin. This process is entirely reversible upon the addition of Zn/Sn granules at room temperature without any added energy. However, the investment of a small amount of renewable energy can speed up the process. This technology demonstrates the facile and efficient capture and recycling of elemental Hg 0 from air. A wide range of metal particles and diverse physicochemical processes, which include the microphysics of nucleation, are herein examined to explore the potential reaction mechanism by using a suite of complementary analytical techniques. These new mechanistic insights open a new era of energy‐neutral environmental remediation based on natural soil/airborne particles.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.151
Threshold uncertainty score0.311

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 head0.008
GPT teacher head0.248
Teacher spread0.240 · 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

Citations10
Published2019
Admission routes2
Has abstractyes

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