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Record W2310022864 · doi:10.4271/2005-01-2994

A Slurry-Based Photocatalytic Reactor with Slurry Separation for Water Recovery

2005· article· en· W2310022864 on OpenAlexaboutno aff
William L. Kostedt, Mickal A. Witwer, David W. Mazyck, Tony Powell, Brian Butters

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
FundersChongqing Science and Technology CommissionNational Aeronautics and Space Administration
KeywordsSlurryPhotocatalysisSeparation (statistics)Materials scienceWaste managementEnvironmental scienceProcess engineeringEnvironmental engineeringComputer scienceChemistryEngineeringCatalysis

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">Currently, proposed water recovery systems for baseline space missions consist of integrated technologies to remove contaminants from graywater for reuse. Lacking in these mission scenarios and in current research efforts is a solid understanding of how photocatalysis might perform as a primary and/or secondary processor. However, one of the major hurdles for slurry-based photocatalysis is the ability to separate the catalyst from solution after mineralization of pollutants is complete. Purifics, a Canadian engineering company, has solved this problem with a patented separation device utilizing a backpressure cycled membrane and automated system (Photo-Cat®). Purifics specifically designed a pilot unit to be used to solve the water recovery problem for long-term space missions. Operating Purifics’ Photo-Cat® as a secondary processor, with and without ammonium bicarbonate demonstrated that the TOC concentration could be reduced to below 0.5 ppm. Preliminary studies treating gray water, hence serving as a primary processor, have been promising.</div>

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.949
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.010
GPT teacher head0.250
Teacher spread0.239 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2005
Admission routes1
Has abstractyes

Explore more

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