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Record W4317809689 · doi:10.3390/fermentation9020092

Mutagenesis of Novel Clostridial fusants for Enhanced Green Biobutanol Production from Agriculture Waste

2023· article· en· W4317809689 on OpenAlexaff
Pallavi Roy, Yaser Dahman

Bibliographic record

VenueFermentation · 2023
Typearticle
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsMutagenesisYield (engineering)ChemistryAcetoneBiofuelButanolIsobutanolBiotechnologyBiologyEthanolMutantBiochemistryGene

Abstract

fetched live from OpenAlex

In an earlier investigation, novel Clostridial fusants were introduced and demonstrated an ability to produce biobutanol at the relatively high temperature of 45 °C. The objective of the present study is to further improve the fused strains through examining the impact of mutation agents on their stability, tolerance to biobutanol toxicity and biofuel production capability. The results for the mutated strains showed enhanced resistance to biobutanol by the fused strains and better biobutanol generation by cells. Furthermore, the results showed high biobutanol production (14.7–15 g/L), with a total Acetone, Biobutanol and Ethanol (ABE) yield of 0.6 g/g. Moreover, mutated strains showed tolerance to biobutanol toxicity up to 15 g/L, which is equivalent to a ~15% increase over literature values. The oxygen tolerance study showed improved performance by the mutated anaerobic fusant. In general, the mutation of fused clostridium strains using UV and EMS leads to the identification of stronger robust strains that show higher tolerance to oxygen and biobutanol toxicity and achieved higher yield.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.022
GPT teacher head0.238
Teacher spread0.216 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations3
Published2023
Admission routes1
Has abstractyes

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