Biobased Bionic Spider Silk via Electrostatic Complexation for Simultaneously Harvesting Atmospheric Water and Triboelectric Energy
Bibliographic record
Abstract
Abstract Atmospheric water harvesting has emerged as a sustainable solution for overcoming water and energy shortages. Herein, an entirely biobased bionic spider silk (chitosan–sodium alginate filament [CSF]) is prepared using an interfacial, aqueous, and straightforward polyelectrolyte complexation with a continuous drawing technique, simultaneously harvesting water and triboelectric energy from ambient humidity. CSF exhibits a periodic spindle‐shaped structure resembling spider silk, with surface roughness conducive to atmospheric water harvesting. The success of the electrostatic complexation technique for CSF is confirmed by water solubility, Fourier‐transform infrared spectroscopy, and thermogravimetric analyses. The production yield of CSF reaches the maximum of 99.36% by controlling the substrate type and polyelectrolyte mass ratio. Moreover, fog‐harvesting efficiency peaks at 1552.83 mg cm −1 h −1 (1.0 wt.% polyelectrolyte concentration), demonstrating concentration‐dependent performance. Subsequently, CSF is woven into a bionic spider web (CSW) for simultaneous water and energy harvesting. Through parametric optimization, the CSW‐based droplet triboelectric nanogenerator system achieves 180 V and 72.25 µW output. When deployed in a high‐humidity greenhouse, the system powers 80 light‐emitting diodes, a hygrometer (thermometer), and a stopwatch. This study presents a straightforward, effective, and green strategy for simultaneously harvesting water and energy from the ambient environment, providing fresh water and renewable energy to enhance sustainability.
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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) | 0.000 | 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 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".