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Record W4388128893 · doi:10.1021/acscentsci.3c00414

Microbial Upcycling of Waste PET to Adipic Acid

2023· article· en· W4388128893 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.

Bibliographic record

VenueACS Central Science · 2023
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsImpact
FundersEngineering and Physical Sciences Research CouncilCarnegie Trust for the Universities of ScotlandUK Research and Innovation
KeywordsAdipic acidTerephthalic acidChemistryEscherichia coliAcetic acidEnzymeBiochemistryOrganic chemistryPolyesterGene

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide Microorganisms can be genetically engineered to transform abundant waste feedstocks into value-added small molecules that would otherwise be manufactured from diminishing fossil resources. Herein, we report the first one-pot bio-upcycling of PET plastic waste into the prolific platform petrochemical and nylon precursor adipic acid in the bacterium Escherichia coli . Optimizing heterologous gene expression and enzyme activity enabled increased flux through the de novo pathway, and immobilization of whole cells in alginate hydrogels increased the stability of the rate-limiting enoate reductase BcER. The pathway enzymes were also interfaced with hydrogen gas generated by engineered E. coli DD-2 in combination with a biocompatible Pd catalyst to enable adipic acid synthesis from metabolic cis, cis -muconic acid. Together, these optimizations resulted in a one-pot conversion to adipic acid from terephthalic acid, including terephthalate samples isolated from industrial PET waste and a post-consumer plastic bottle.

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 categoriesInsufficient payload (model declined to judge)
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 score0.170
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.009
GPT teacher head0.216
Teacher spread0.208 · 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