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Record W3158048517 · doi:10.1002/jctb.6790

Bioderived and degradable polymers for transient electronics

2021· article· en· W3158048517 on OpenAlexafffund
Azalea Uva, Angela Lin, Jon Babi, Helen Tran

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2021
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto
KeywordsElectronicsScope (computer science)Transient (computer programming)Biochemical engineeringComputer scienceNanotechnologyEngineeringMaterials scienceElectrical engineering

Abstract

fetched live from OpenAlex

Abstract As single‐use electronics become more prevalent in our society, a shift towards devices with alternative disposal fates will be required to address rising levels of electronic and plastic waste. Adopting transient electronics is one solution for inadvertent litter of future single‐use electronics as they are designed to automatically break down in environmental conditions after their intended use. However, the selection of appropriate source materials to make these transient devices is vital to ensure environmental compatibility. This mini‐review aims to highlight recent advancements in bioderived polymers that can be used as substrates or encapsulants, the largest weight percentage in a device, in transient electronics. The chemical and biological degradation of these bioderived polymers is also discussed to present potential non‐toxic byproducts and factors affecting degradation rates. Lastly, the potential outlook of transient electronics in biomedical, environmental, and consumer applications are proposed to demonstrate the wide scope of opportunities to be explored. © 2021 Society of Chemical Industry (SCI).

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: none
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.007
GPT teacher head0.211
Teacher spread0.204 · 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

Citations44
Published2021
Admission routes2
Has abstractyes

Explore more

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