Novel Applications of Silk Proteins Based on Their Interactions with Metal Ions
Bibliographic record
Abstract
Silk secreted by Bombyx mori L. silkworm has become one of the most important biomaterials, due to its excellent biocompatibility, controllable biodegradability, superior processability, and unique mechanical properties. Silk fibroin and sericin, as the two components of silk, contain abundant polar functional groups, and thus can bind metal ions through electrostatic interaction and chelation. Based on this binding, silk proteins not only can be used to fabricate ecofriendly and efficient adsorbents to remove heavy metals from waterbodies, but also can synthesize metal nanostructures (nanoparticles or nanoclusters) to form silk/metal composites with amazing optical or electrochemical characteristics. This binding also can be manipulated to optimize silk’s performance. This review focuses on discussing and summarizing advances in the use of silk fibroin and sericin for heavy metal ion-contaminated water remediation, biosensing materials, and electrochemical materials from the perspective of the interaction between silk proteins and metal ions. The performance enhancement of silk using metal ions is also analyzed. Moreover, the silk proteins’ interactions with metal ions and related structural features that contribute to the above sustainable applications are illustrated to lay a theoretical foundation. This review will favor the expansion of the applications of silk in both the traditional textile field and new biomaterials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".