Inkjet Printing for Batteries and Supercapacitors: State-of-the-Art Developments and Outlook
Bibliographic record
Abstract
Inkjet printing enables contactless deposition onto fragile substrates for printed energy-storage devices and supports flexible batteries and supercapacitors with reduced material use. This review examines multilayer and interdigital architectures and analyzes how ink rheology, droplet formation, colloidal interactions, and the printability window govern performance. For batteries, reported inkjet-printed electrodes commonly deliver capacities of ~110–150 mAh g−1 for oxide cathodes at C/2–1 C, with coulombic efficiency ≥98% and stability over 102–103 cycles; silicon anodes reach ~1.0–2.0 Ah g−1 with efficiency approaching 99% under stepwise formation. Typical current densities are ~0.5–5 mA cm−2 depending on areal loading, and multilayer designs with optimized drying and parameter tuning can yield rate and discharge behavior comparable to cast films. For supercapacitors, inkjet-printed microdevices report volumetric capacitances in the mid-hundreds of F cm−3, translating to ~9–34 mWh cm−3 and ~0.25–0.41 W cm−3, with 80–95% retention after 10,000 cycles and coulombic efficiency near 99%. In solid-state configurations, stability is enhanced, although often accompanied by reduced areal capacitance. Although solids loading is lower than in screen printing, precise material placement together with thermal or photonic sintering enables competitive capacity, rate capability, and cycle life while minimizing waste. The review consolidates practical guidance on ink formulation, printability, and defect control and outlines opportunities in greener chemistries, oxidation-resistant metallic systems, and scalable high-throughput printing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".