An investigation of a wash‐durable solar energy harvesting textile
Bibliographic record
Abstract
Abstract This work demonstrates a novel and sustainable energy solution in the form of a photovoltaic fabric that can deliver a reliable energy source for wearable and mobile devices. The solar fabric was woven using electronic yarns created by embedding miniature crystalline silicon solar cells connected with fine copper wires within the fibres of a textile yarn. This approach of integrating solar energy harvesting capability within the heart of the textile fabric allows it to retain the flexibility, three‐dimensional deformability, and moisture and heat transfer characteristics of the fabric. In this investigation, both the design and performance of the solar cell embedded yarns and solar energy harvesting fabrics were explored. These yarns and resultant fabrics were characterised under different light intensities and at different angles of incident light, a critical factor for a wearable device. The solar cell embedded yarns woven into fabrics can undergo domestic laundering and maintained ~90% of their original power output after 15 machine wash cycles. The solar fabric embedded with 200 solar cells demonstrated here (44.5 mm × 45.5 mm active area) was capable of continuously generating ~2.15 mW/cm 2 under one sun illumination and was capable of powering a basic mobile phone. The power generation capability and durability of the solar energy harvesting fabric proved its viability to power wearable devices as an integral part of regular clothing.
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 | 0.000 | 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".