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Record W3025096979 · doi:10.1149/ma2020-01522933mtgabs

Investigation of Borohydride/Peroxide Desalination Cell in a Batch Regime

2020· article· en· W3025096979 on OpenAlexaff
Shawn Nicholas Hamilton, Amarjeet Bassi, Dimitri Karamanev

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsWestern University
Fundersnot available
KeywordsDesalinationMembraneSalt (chemistry)BorohydrideNafionChemistryElectrochemistryRedoxIon exchangeNuclear chemistryChemical engineeringInorganic chemistryCatalysisIonOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Road salt has an environmental impact on the environment. According to one road salt monitoring organization, The Salt Institute, it is estimated that in North America alone, 6 million tonnes is dispersed annually. It has caused a negative influence on biotic life forms in freshwaters as well as the deterioration of infrastructure such as bridges and buildings through increased steel corrosion. In this work, a batch lab-scale electrochemical desalination cell (EDC) was studied to reduce the salt through utilizing the energy from NaBH 4 /H 2 O 2 redox reaction. This 3-compartment borohydride/NaCl/peroxide cell was effective in desalinating different road salts: NaCl, CaCl 2, MgCl 2 and KCl. Combinations of different anion exchange membranes (AMI-7001; FAB-PPS-130) with different cation exchange membranes (CMI-7000; Nafion ® 115; Nafion ® 117) were studied. There were differences in desalination rate with varying pairings of membranes. Over 5 hours, the conductivity of a solution in batch regime has decreased by over 42%. The EDC’s maximum current and power densities were 5.32 A/m 2 and 2.75 W/m 2 , respectively. These results have demonstrated the successful operation of an EDC using NaBH 4 and H 2 O 2 in batch regime.

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 score0.108
Threshold uncertainty score0.628

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 head0.024
GPT teacher head0.229
Teacher spread0.205 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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