(Invited) Wearable Microfluidic and Electronic Frameworks for Biomedical Applications
Bibliographic record
Abstract
I. Introduction Advances in wearable electronics, functional nanocomposite polymers, flexible microfluidics, and commercial polymer microfluidics are reported on a daily basis. However, much of this research is field-specific, despite the enormous potential that the convergence of these research areas has for the future of health care, worker safety, food safety, consumer devices, and commercial microfluidics. We present an innovative approach of powerful, general-purpose frameworks that facilitate interfacing of nanocomposite polymer-based, microfluidic, and electronic devices on flexible polymer, textile, or industrial polymer platforms. Such approaches offer practical solutions for areas such as safety lighting and health monitors for safety vests; heart monitors and perspiration monitors for athletic clothing; and other applications in real-time wearable bioelectric and biochemical monitoring. II. Wearable Microfluidic and Electronic Frameworks A. Wearable bioelectric sensors Intense research into wearable electronics has resulted in many innovative devices and systems, e.g.: flexible polyimide or Kapton printed circuit boards (PCBs) [1]; roll-to-roll foil and other printed devices [2]; printing and weaving of textiles [3]; and special geometries for flexible interconnect between rigid components [4]. We have investigated conductive nanocomposite polymers and metal transfer processes for wearable bioelectric sensors [5, 6]. These technologies can be employed in, for example, conformable and wearable systems for electrocardiogram (ECG) [5], tissue impedance [7] and pressure [8] sensors. Unlike many other techniques, the technology and materials employed for our devices are highly compatible with clothing-based textiles. B. Wearable microfluidics Much less research has been performed in the emerging area of wearable microfluidics. Traditionally, microfluidic devices are fabricated in rigid substrates or flexible materials such as polydimethylsiloxane (PDMS) that are bonded to rigid substrates. Free-standing PDMS devices are developed for, e.g., perspiration sensors [9]; however, such processes typically require long fabrication times, equipment in a cleanroom facility, and difficulty with integration onto textiles. Other devices employ porous materials or microneedles to deliver biofluids, e.g., perspiration or interstitial fluid, by capillary forces [10]; however, such devices are limited in fluid collection or are too invasive. Wearable textile-based devices have been recently demonstrated that use the textile itself as a fluidic “channel” [11]; however, such devices are dependent on the fluidic characteristics of the fabric. To overcome these limitations and facilitate the development of fully flexible, wearable, and durable microfluidic devices that can be used for a wider variety of applications, we have developed a printing-based fabrication process that employs screen printable plastisol ink [12]. This process is demonstrated for simple multi-level microfluidic devices toward the goal of fully wearable microfluidic systems. III. Summary Many methods have been developed for microelectronic and microfluidic devices in rigid or flexible packages (e.g., [13, 14, 15]). However, such technologies suffer from difficult integration and/or limited flexibility, limited geometries, limited microfluidic sample size, and/or difficult integration, especially on textile-based substrates. The technologies we present offer simple and compatible printing-based and transfer processes for microelectronic and microfluidic frameworks that facilitate cost-effective development of durable wearable biomedical systems. References Y. Chuo, et. al., IEEE Transactions Biomedical Circuits Systems 4(5):281-94 (2010). K. Jain, et. al., Proceedings of the IEEE 93(8):15000-10 (2005). F. Carpi, et. al., IEEE Transactions Information Technology Biomedicine 9(3):295-318 (2005). M. Gonzalez, et. al., Microelectronics Reliability 51:1069-76 (2011). D. Chung, et. al. Proc. SPIE 9060, (2014); doi:10.1117/12.2046548. D. Hilbich, et. al., Proc. SPIE 98020R (2016); doi:10.1117/12.2219284. D. Chung, et. al., J Electrochemical Society 161(2):B3071-76 (2014). A. Rahbar, et. al., Proc. SPIE 90600O (2014); doi:10.1117/12.2044342. J. Choi, et. al., Adv Healthcare Mater 6:1601355 (2017). F. Benito-Lopez, et. al., Procedia Chemistry 1(1):1103-1106 (2009). A. Nilghaz, et. al., Lab Chip 12:209-218 (2012). D. Chung & B.L. Gray, J Micromech Microeng, 27(11), (2017). S. Cheng., et. al., Advanced Functional Materials 21(12):2282-90 (2011). M. Scholles, et. al., Proc. of SPIE 7593:75930C-2 (2013). A. Wu, et. al., Lab Chip 10:519-521 (2010).
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.091 | 0.037 |
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".