Microsized Electrochemical Energy Storage Devices and Their Fabrication Techniques For Portable Applications
Bibliographic record
Abstract
Abstract Over the last decade, Lab‐on‐chip (LOC) technology has been thriving to support the ever‐increasing demand of high‐throughput, fast, accurate, and reliable analysis in an extensive variety of miniaturized systems for medical, chemical, and biological applications. Furthermore, portable electronics and consumer devices such as cell phones, tablets, smart watches, point‐of‐care devices, wireless sensor nodes, radio frequency identification, and other gadgets have witnessed a tremendous demand worldwide. These fast‐paced technologies have an intimate correlation with the booming research activity in micro‐supercapacitors (MSCs) and microbatteries (MBs); two energy storage devices which have claimed the lion's share in powering LOC components and other portable devices. In this review, MSCs and MBs are presented with highlights on their main components, structure, and types, as well as their state‐of‐the‐art performance capabilities. The recent efforts in fabrication strategies, mainly those compatible with device fabrication techniques, stating the advantages and limitations of each are also reviewed. The paper also emphasizes the need for a benchmarking standard upon which performance is compared, as scholarly work shows a discrepancy in the use of different performance metrics to describe the electrochemical performance of such devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".