3D hollow-structured hydrogels with editable macrostructure, function, and mechanical properties induced by segmented adjustments
Bibliographic record
Abstract
Currently, it is challenging to prepare uniform hollow-structured hydrogels with tailorable comprehensive properties. Herein, making full use of the different gelation routes of polyvinyl alcohol (PVA), we propose a distinctive two-stage method for preparing hollow-structured hydrogels, which is to arrange the microstructure of the hydrogel through segmented adjustment. The mechanical properties, macrostructure, and functions of the obtained hollow hydrogel can be easily designed and edited. Specifically, the mechanical properties of the hollow hydrogel can be improved from "soft" to "hard" by changing the preparation conditions. In addition, hollow hydrogels with diverse macrostructures can also be developed through different templates, such as tubes, gloves, and rings. More importantly, the hollow hydrogels can be endowed with conductive, anti-drying, anti-freezing, and photothermal-converting functions due to the great system compatibility of the gel precursor. Benefiting from the advantages of the hollow hydrogel, the conductive gel ring-based bioelectrodes and sensors were developed. Interestingly, the adaptive gel ring-based electronics can stably record the electrophysiological and strain signals of the human body without the help of adhesive tape. This study opens more opportunities for development and applications of other hydrogel-based hollow materials.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".