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Record W4402135020 · doi:10.1002/adma.202401560

Designing for Degradation: Transient Devices Enabled by (Nano)Cellulose

2024· review· en· W4402135020 on OpenAlexafffund
Lucas J. Andrew, Erlantz Lizundia, Mark J. MacLachlan

Bibliographic record

VenueAdvanced Materials · 2024
Typereview
Languageen
FieldMaterials Science
TopicAdvanced Cellulose Research Studies
Canadian institutionsUniversity of British Columbia
FundersEuskal Herriko UnibertsitateaEusko JaurlaritzaCanada Research Chairs
KeywordsCellulosic ethanolMaterials scienceElectronicsTransient (computer programming)CelluloseNanotechnologyHazardous wasteComputer scienceEngineeringElectrical engineeringWaste management

Abstract

fetched live from OpenAlex

Transient technology involves materials and devices that undergo controlled degradation after a reliable operation period. This groundbreaking strategy offers significant advantages over conventional devices based on non-renewable materials by limiting environmental exposure to potentially hazardous components after disposal, and by increasing material circularity. As the most abundant naturally occurring polymer on Earth, cellulose is an attractive material for this purpose. Besides, (nano)celluloses are inherently biodegradable and have competitive mechanical, optical, thermal, and ionic conductivity properties that can be exploited to develop sustainable devices and avoid the end-of-life issues associated with conventional systems. Despite its potential, few efforts have been made to review current advances in cellulose-based transient technology. Therefore, this review catalogs the state-of-the-art developments in transient devices enabled by cellulosic materials. To provide a wide perspective, the various degradation mechanisms involved in cellulosic transient devices are introduced. The advanced capabilities of transient cellulosic systems in sensing, photonics, energy storage, electronics, and biomedicine are also highlighted. Current bottlenecks toward successful implementation are discussed, with material circularity and environmental impact metrics at the center. It is believed that this review will serve as a valuable resource for the proliferation of cellulose-based transient technology and its implementation into fully integrated, circular, and environmentally sustainable devices.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.681
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.057
GPT teacher head0.365
Teacher spread0.308 · 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.

Study designBench or experimental
Domainnot available
GenreReview

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

Citations30
Published2024
Admission routes2
Has abstractyes

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