Self‐Sustained Electricity Generation from Ambient Moisture Enabled by Step‐Gradient Polysaccharide Films
Bibliographic record
Abstract
Abstract Moisture electricity generator (MEG) has emerged as a promising technology to directly harvest energy from the ambient atmosphere. However, their practical applications remain limited by inefficient ion transport. In this work, a high‐performance, polysaccharide‐based step‐gradient MEG (SG‐MEG) is presented, fabricated by the sequential multilayer casting‐drying of polysaccharide materials with varying functional group densities. This design creates an intrinsic step‐wise ionic gradient, promoting ion dissociation and diffusion. As a result, a single unit can sustain an open‐circuit voltage of 0.78 V for over 1300 h, a milliamp‐scale short‐circuit current density of 1.1 mA cm −2 and an improved power density of 15.49 µW cm −2 (1291 W m⁻ 3 ), representing a ten‐fold enhancement compared to conventional MEG. Numerical simulations and experimental analyses confirm that the enhanced performance arises from the high functionalization of polysaccharide and the facilitated ion diffusion enabled by the step gradient.The multifunctionality of SG‐MEG is demonstrated in practical applications, including self‐powered respiration monitoring, direct powering of low‐power electronics, and integration with energy storage systems for autonomous devices such as a self‐cruising boat and a water‐level alarm. This work offers a new paradigm for the design of high‐performance, bio‐based MEGs and advances their application.
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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".