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Record W4399420291 · doi:10.1002/9783527839865.ch15

Lignin‐based Hydrogel: Mechanism, Properties, and Applications

2024· other· en· W4399420291 on OpenAlexaff
Qiang Wang, Baobin Wang, Jiachuan Chen, Guihua Yang, Lei Zhang, Kefeng Liu, Qimeng Jiang, Pedram Fatehi

Bibliographic record

Venuenot available
Typeother
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsLakehead University
Fundersnot available
KeywordsLigninMechanism (biology)Polymer scienceChemical engineeringMaterials scienceChemistryEngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Hydrogels have attracted attention due to their tunable properties, which make them applicable in many fields, such as wastewater treatment, drug delivery, wound dressing, agriculture, and energy storage materials. Lignin, as the second largest abundant renewable aromatic resource, could be incorporated into the hydrogel matrix for endowing versatile functions, such as high-water absorption capability, good mechanical strength, and UV-shielding performance. This chapter focuses on the production approaches and applications of lignin-based hydrogels and the future perspective of lignin-based hydrogels. In addition, the function of lignin in lignin-based hydrogels was discussed comprehensively. Generally, the poly phenol structure of lignin enables various properties, such as anti-UV blocking, antimicrobial, and antioxidative, facilitating the application of lignin-containing hydrogels in dye and heavy metal removals, drug carrier, wound dressing, watering plants, and energy storage. Finally, the prospect for the future development of lignin-based hydrogel was elaborated.

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 score0.002
Threshold uncertainty score0.007

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.011
GPT teacher head0.183
Teacher spread0.173 · 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

Citations4
Published2024
Admission routes1
Has abstractyes

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