Recent Advances in the Nanomaterials, Design, Fabrication Approaches of Thermoelectric Nanogenerators for Various Applications
Bibliographic record
Abstract
Abstract Thermoelectric nanogenerators (TENGs) are promising sustainable energy devices that utilize thermoelectric (TE) effect of nanomaterials to convert a temperature gradient into electrical energy. Compared to bulk thermoelectric generators (TEGs) that are commercially available, TENGs are more flexible and power‐dense, owing to their tuneable nanostructures. Hence, smaller TENGs are better suited for small form‐factor applications like wearable electronics, internet of things (IoT) devices, and self‐powered sensors. However, the higher complexity and cost of TENGs than TEGs inhibit their widespread adoption. This review appraises the latest advances in TENG materials, design, and fabrication in optimizing the performance of TENGs, making TENGs more viable for real‐world applications. More precisely, this work examines how nanostructure engineering, nanomaterial compositing, and post‐synthesis treatment approaches have enhanced the TE properties of common and promising TE materials, including tellurides, selenides, metal oxides, metal alloys, silicon, carbon nanomaterials, and organic compounds. Given that the TE material is a key component in TENGs, this review highlights how to optimize other vital parameters, including the TENG configuration, contact interface, form factor, heat sink use, and folded shape for specific applications. Lastly, critical attributes of TENGs used in wearable electronics, sensors, implantable electronics, solar energy conversion, and waste heat recovery are analyzed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".