MétaCan
Menu
Back to cohort
Record W4411586738 · doi:10.1021/acsestengg.5c00163

Simultaneous Biogas Upgrading and Desulfurization Using a Microbial Electrosynthesis System with Optimized Electrodes and Membrane Selection

2025· article· en· W4411586738 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS ES&T Engineering · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsElectrosynthesisFlue-gas desulfurizationBiogasElectrodeSelection (genetic algorithm)MembraneChemical engineeringWaste managementChemistryPulp and paper industryProcess engineeringEnvironmental scienceBiochemical engineeringMaterials scienceComputer scienceEngineeringElectrochemistryBiochemistryArtificial intelligence

Abstract

fetched live from OpenAlex

Biogas upgrading based on the principle of the microbial electrosynthesis (MES) system offers a promising avenue for biogas upgrading. Here, we explored 4 different combinations of cathode and membrane materials to optimize MES for biogas upgrading. MES equipped with a stainless steel cathode and Nafion 117 membrane (designated as MES-2) demonstrated optimal performance, achieving a maximum methane production of 268.5 ± 19.5 L methane /m cathode 3 with a bicarbonate medium. Furthermore, MES-2 showed superior performance with a CO 2 -rich gas (70% CO 2 and 30% N 2 ), achieving 100% CO 2 conversion to methane conversion after 3 days of gas recirculation. When testing different biogas sources (synthetic and real anaerobic digestion biogas), MES-2 also consistently provided >99% methane content within a relatively short time (<3 days) of biogas recirculation. Additionally, H 2 S content was significantly reduced from 214 ppmv to <1 ppmv, enabling the upgraded biogas to be widely utilized in various applications. The microbial community analysis indicated that this outcome was primarily due to the substantial growth of chemolithoautotrophic sulfide-oxidizing bacteria, such as Thiobacillus, which likely converted sulfide to elemental sulfur and/or sulfate. This study underscores the potential of MES as a highly effective and uniquely adaptable technology for biogas upgrading and desulfurization, promoting sustainable energy practices.

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.

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 score0.297
Threshold uncertainty score0.547

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 head0.002
GPT teacher head0.163
Teacher spread0.161 · 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