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Record W4319599189 · doi:10.1002/9781119829584.ch10

Continuous Production of Clean Hydrogen from Wastewater by Microbial Usage

2023· other· en· W4319599189 on OpenAlexaff
P. Satishkumar, Arun M. Isloor, Ramin Farnood

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBiohydrogenHydrogen productionDark fermentationWastewaterEnvironmental scienceBioreactorMicrobial electrolysis cellChemical oxygen demandRenewable energyWaste managementPulp and paper industryElectrolysisFermentationBiochemical engineeringHydrogenChemistryEnvironmental engineeringEngineeringEcologyBiologyFood science

Abstract

fetched live from OpenAlex

Biohydrogen production from wastewater is a prominent way to address escalating global energy demand and alarming environmental pollution. The need for renewable, sustainable, economic, and environment-friendly pathways for energy generation is fulfilled by biohydrogen evolution. Wastewaters contain a vast array of organic contents, as well as microbes and are a suitable source for bioreactors. Treatment of wastewaters with hydrogen-generating bacteria significantly aids its purification process by reducing chemical oxygen demand with simultaneous hydrogen generation. Among the various methods that are available for hydrogen production from microbes, photo fermentation, dark fermentation, and microbial electrolysis cells are discussed thoroughly. Continuous hydrogen generation systems are most suitable for large scale commercial production. Uniform product quality is obtained in the case of continuous systems. Microbial electrolysis cells have been found to yield exceptionally good hydrogen purity. A variety of factors that affect hydrogen evolution in all the techniques are reviewed in detail.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.256
Threshold uncertainty score0.998

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)0.0260.003

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 head0.005
GPT teacher head0.181
Teacher spread0.175 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations4
Published2023
Admission routes1
Has abstractyes

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