Screen Printing for Energy Storage and Functional Electronics: A Review
Bibliographic record
Abstract
Printed electronics employ established printing methods to create low-cost, mechanically flexible devices including batteries, supercapacitors, sensors, antennas and RFID tags on plastic, paper and textile substrates. This review focuses on the specific contribution of screen printing to that landscape, examining how ink viscosity, mesh selection and squeegee dynamics govern film uniformity, pattern resolution and ultimately device performance. Recent progress in advanced ink systems is surveyed, highlighting carbon allotropes (graphene, carbon nano-onions, carbon nanotubes, graphite), silver and copper nanostructures, MXene and functional oxides that collectively enhance mechanical robustness, electrical conductivity and radio-frequency behavior. Parallel improvements in substrate engineering such as polyimide, PET, TPU, cellulose and elastomers demonstrate the technique’s capacity to accommodate complex geometries for wearable, medical and industrial applications while supporting environmentally responsible material choices such as water-borne binders and bio-based solvents. By mapping two decades of developments across energy-storage layers and functional electronics, the article identifies the key process elements, recurring challenges and emerging sustainable practices that will guide future optimization of screen-printing materials and protocols for high-performance, customizable and eco-friendly flexible devices.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".