Bibliographic record
Abstract
This dissertation presents a comprehensive study on the design and development of a self-powered smart wearable device in the form of clothing for non-invasive continuous monitoring of health conditions such as glucose and body movements. The designed wearable device comprises three key components: an embedded biosensor unit, a clean energy harvesting module, and a flexible energy storing device. Two types of sensors are developed to monitor health biomarkers with precision. The first sensor utilizes non-enzymatic nanomaterials, such as Co/Cu nanostructures arrayed with functionalized multiwall carbon nanotubes (F-MWCNT)/Fe3O4, for glucose detection. Real sweat samples at physiological pH were tested, demonstrating clinical accuracy comparable to commercial glucometers. The second sensor is a thread-based motion sensor that incorporates a modified PVA hydrogel with hydroxyl functionalized MXene and hBN. This sensor offers exceptional sensitivity and self-healing capabilities, making it ideal for accurate body movement monitoring and potential applications in human machine interactions. To enable continuous operation, the energy required by sensors is harvested from body movements. This is achieved using flexoelectric and piezoelectric principles. By incorporating functionalized hexagonal boron nitride (F-hBN) with polyvinylidene fluoride (PVDF), the energy harvesting performance is significantly enhanced, resulting in a 5.5 fold increase in output voltage during open-circuit tests, reaching 23 V. To store the generated electricity, an energy-harvesting module must be connected to an energy-storing unit. In this dissertation, flexible supercapacitors are created using a two step process. First, high-performance gel polymer electrolytes (HP-GPEs) are developed, considering the influence of rheology and ion conduction mechanism on ionic conductivity. Second, functionalized hBN (FhBN) nanosheets are utilized to fabricate the flexible supercapacitors, resulting in a six-fold increase in ionic conductivity. The integration of ion-conductive nanosheets enhances ion transfer and energy storage capabilities, demonstrating exceptional cyclability with over 80% capacity retention after 50,000 cycles. By creating the concept of self-powered wearable platforms and addressing their three major challenges (i.e., sensors, energy harvesting, and energy storage), this research contributes to the advancement of non-invasive, continuous, and real-time health monitoring. The contributions include the design and development of novel electrocatalysts, morphological approaches for hydrogel-based sensors, nano engineered energy harvesting materials, gel polymer electrolytes, and piezoelectric nanocomposites. Through comprehensive analysis, including microscopic, structural, spectroscopic, chemical, morphological, and electrochemical assessments, this dissertation provides valuable insights and practical applications for the implementation of self-powered wearable devices. These findings pave the way for improved health condition monitoring.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".