What the Emerging Generation of Researchers Think Is Hot in Sensors
Bibliographic record
Abstract
What the Emerging Generation of Researchers Think Is Hot in SensorsACS Sensors, like all ACS journals, seeks to represent the full spectrum of its readership, including the PhD students and postdocssuch as ourselves, who are working at the bench.At ACS Sensors, our perspective is partly presented through the Twitter feed, as we are your Twitter editors.We are based in Sydney, Geneva, Shanghai, Tempe, and Toronto, in the laboratories of some of the ACS Sensors editorial team.We actually have contracts, with the ACS, that stipulate that we are each responsible for 1 day of tweets per week; so, from our tweets, you can get an inkling of which areas of sensors we find particularly interesting.However, it was suggested that we write an editorial to share with you what areas of sensing we are really excited by.Since we are spread around the globe, and cannot easily get together, we independently wrote and submitted paragraphs about the sensing topics covered in ACS Sensors, and other outlets, that inspired us.Our EIC and Associate Coordinating Editor, Sue Liu, in Sydney then collated the ideas, and looked for common threads in our submissionswe had an awful lot in common!The areas of sensing that really appealed to all of us were the emerging areas of paper-based sensors and wearable sensors.In fact, all five of us commented on paper sensors.So, from the more than 300 papers that have been published in ACS Sensors so far, several were chosen by more than one of us as standout papers, and they all relate to wearable sensors and paper-based sensors, which are discussed below.Related to these two broad areas was an interest in both integration and miniaturization.Why are our interests so aligned?We are guessing, but we think this is because sensors are starting to become very pervasive in our everyday lives.We see this, of course, through the smart watches many of us wear.These are not chemical sensors, however, and the information they provide is, at the very best, a surrogate for chemical information.The wearable sensor papers that resonated included "Noninvasive Alcohol Monitoring Using a Wearable Tattoo-Based Iontophoretic-Biosensing System" (DOI: 10.1021/ acssensors.6b00356) by Joseph Wang and co-workers from University of California in San Diego.We really liked the way alcohol could be detected in sweat, and the user being able to simply read off the information on a smartphone.Another wearable sensor paper that caught the imagination of more than one of us was a stretchable sensor for monitoring human motion entitled "Rapid-Response, Widely Stretchable Sensor of Aligned MWCNT/Elastomer Composites for Human Motion Detection" by Yoku Inoue and co-workers from Shizoku University and the Yamaha Corporation (DOI: 10.1021/ acssensors.6b00145).We also liked a very recent paper on incorporating flexible sensors with chemical sensors, also by Joseph Wang's group at UCSD, entitled "Wearable Flexible and Stretchable Glove Biosensor for On-Site Detection of Organophosphorus Chemical Threats" (
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".