Hierarchically Porous Cellulose Membrane via Self‐Assembly Engineering for Ultra High‐Power Thermoelectrical Generation in Natural Convection
Bibliographic record
Abstract
Abstract Renewable heat‐to‐power conversion based on thermoelectric strategy holds strong prospect toward clean electricity generation in low‐carbon society, in which its conversion performance is mainly decided by the temperature gradient. However, achieving a high temperature gradient spontaneously throughout the day in natural convection remains a significant challenge. Herein, cost‐effective, sustainable, and hierarchically porous cellulose membrane (HPCM) created through a simple self‐assembly engineering of cellulose molecules is proposed. Such HPCM boasts a unique structure of layered micro‐ and nanoscaled pores with ≈95% porosity, and correspondingly demonstrates >94% solar reflectance and >0.9 mid‐infrared emissivity. As a result, HPCM enables average temperature gradient of 14.5 °C and 76 mV output voltage of thermoelectric module during daytime natural convection, which are 17‐ and 30‐time higher than those of pristine device, respectively. Note that HPCM‐based thermoelectric module consistently generates an average output voltage of 44.2 mV all day. Such modules are seamlessly integrated into thermoelectric arrays to achieve high output voltage of ≈1.5 V and power density of ≈3 W m −2 over 90‐d period. The prepared HPCM marks a significant advancement in environmentally friendly, scalable, and viable thermoelectric conversion to power the low‐carbon society.
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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".