4D Printing for Energy Storage Systems: A Transformative Manufacturing Paradigm
Bibliographic record
Abstract
With growing demands in the field of energy storage materials and the need to manufacture more adaptive, efficient, and sustainable systems, there has been increased interest in advanced manufacturing technologies that enable both structural programmability and functional responsiveness. 4D printing (4DP), an evolution of additive manufacturing, uses stimuli-responsive smart materials (such as shape memory polymers, hydrogels, nanocomposites, and metal oxides) to fabricate components that are capable of time-dependent dynamic reconfiguration. This study comprehensively investigates the intersection of 4DP technology and energy storage systems by critically evaluating the materials, processes, and device-centric applications of 4DP in batteries, supercapacitors, and fuel cells. This study categorizes electrochemical storage types, their material requirements, and current synthesis methods systematically, identifying key limitations in energy efficiency, waste, and adaptability. 4DP-compatible materials are thoroughly analyzed for their printability, structural integrity, and functional performance under various stimuli, as demonstrated in multiple case studies, enabling thermal actuation, shape recovery, and self-healing in energy devices. A comparative analysis was conducted between 3DP technology and 4DP in terms of parameters such as energy consumption, material waste, flexibility, and scalability. Current technological barriers identified in the literature include low throughput, complexities in ink formulation, and postprint activation requirements, which are discussed along with emerging solutions. With this review, the authors position 4DP as a technology not only as a potential alternative fabrication method but also as a transformative paradigm for next-generation energy storage systems that are programmable, multifunctional, and sustainable.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".