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Record W2913436408 · doi:10.3390/en12030530

Seed Pretreatment for Increased Hydrogen Production Using Mixed-Culture Systems with Advantages over Pure-Culture Systems

2019· article· en· W2913436408 on OpenAlexafffund
Vinayak Laxman Pachapur, Prianka Kutty, Preetika Kuknur Pachapur, Satinder Kaur Brar, Yann Le Bihan, Rosa Galvez‐Cloutier, Gerardo Buelna

Bibliographic record

VenueEnergies · 2019
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsUniversité LavalCentre de Recherche Industrielle du QuébecYork UniversityInstitut National de la Recherche Scientifique
FundersMitacs
KeywordsHydrogen productionEnergy carrierBioconversionSterilization (economics)Pulp and paper industryHydrogenProcess engineeringBiohydrogenFermentative hydrogen productionChemistryAerationBiochemical engineeringEnvironmental scienceWaste managementBiotechnologyFood scienceFermentationBusinessEngineeringOrganic chemistryBiology

Abstract

fetched live from OpenAlex

Hydrogen is an important source of energy and is considered as the future energy carrier post-petroleum era. Nowadays hydrogen production through various methods is being explored and developed to minimize the production costs. Biological hydrogen production has remained an attractive option, highly economical despite low yields. The mixed-culture systems use undefined microbial consortia unlike pure-cultures that use defined microbial species for hydrogen production. This review summarizes mixed-culture system pretreatments such as heat, chemical (acid, alkali), microwave, ultrasound, aeration, and electric current, amongst others, and their combinations to improve the hydrogen yields. The literature representation of pretreatments in mixed-culture systems is as follows: 45–50% heat-treatment, 15–20% chemical, 5–10% microwave, 10–15% combined and 10–15% other treatment. In comparison to pure-culture mixed-culture offers several advantages, such as technical feasibility, minimum inoculum steps, minimum media supplements, ease of operation, and the fact it works on a wide spectrum of low-cost easily available organic wastes for valorization in hydrogen production. In comparison to pure-culture, mixed-culture can eliminate media sterilization (4 h), incubation step (18–36 h), media supplements cost ($4–6 for bioconversion of 1 kg crude glycerol (CG)) and around 10–15 Millijoule (MJ) of energy can be decreased for the single run.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.814
Threshold uncertainty score0.735

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 head0.006
GPT teacher head0.199
Teacher spread0.193 · 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 designSimulation or modeling
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

Citations74
Published2019
Admission routes2
Has abstractyes

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