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Record W4386996050 · doi:10.3390/min13101247

Impact of Long-Term Exposure to High Chlorine and to Low pH Solutions during Chlorine Regeneration of Ammonia-Loaded Zeolite

2023· article· en· W4386996050 on OpenAlexafffund
Roberto Narbaitz, Tianguang Zhang, Grace Oluwatomisin Akerele, Majid Sartaj, Jason Downey

Bibliographic record

VenueMinerals · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsZeoliteChlorineAmmoniumAmmoniaChemistryAdsorptionInorganic chemistryNuclear chemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

An earlier study has shown that chlorine solutions were capable of effectively regenerating an ammonium-loaded zeolite column; however, the chlorine concentrations were high (1000 mg Cl2/L), and for two hours of the regeneration cycle, the pH was approximately 3. This led to concerns regarding the long-term durability of the zeolite. The objective of this study is to investigate the durability of a zeolite by conducting long-term batch exposure tests using (a) high concentration chlorine solutions and (b) low pH solutions. Particle size analysis, SEM images, N2 gas adsorption tests, FTIR characterization and batch loading tests showed that 35-day exposure to 1000 mg Cl2/L solutions did not significantly impact the zeolite studied. This chlorine exposure is equivalent to 840,000 ppm-h, which is three orders of magnitude higher than the values recommended by the supplier. The 90-day-long low pH exposure tests showed that pH = 4 solutions only slightly impacted the zeolite’s characteristics and ammonium uptake; however, the pH = 3 exposure led to discernable changes, and the pH = 2 exposure led to an even greater impact. At pH = 2, there was a breakdown of some external part of the zeolite particles, leading to a 7.1-fold increase in the fines and a 56% reduction in the ammonium uptake. The decrease in the ammonium uptake was proportional to the percent of fines.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.446

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 head0.018
GPT teacher head0.238
Teacher spread0.220 · 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 designObservational
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

Citations2
Published2023
Admission routes2
Has abstractyes

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