Detectors and light-sources for optical spectrometry: from a 3D-printed light-source to a self-powered sensor fabricated on a flexible polymeric substrate, and from there on to an IoT-enabled "smart" system
Bibliographic record
Abstract
We are developing detectors to sense the visible part of the spectrum. We are also developing light-sources that generate spectral signals from micro-samples introduced (for compatibility reasons) into micro-plasmas. Our battery- operated microplasmas are coupled to a portable, fiber-optic spectrometer and this combination (or system) can be thought of as a "multi parameter" or "multi-element sensor" for the UV and the Vis parts of the spectrum. Initially, our Micro Plasma Devices (MPDs) were fabricated using technologies borrowed from the semiconductor industry (e.g., microfluidics, micromachining) [1] . To reduce fabrication costs and to enable rapid prototyping [2] , we fabricated Micro Plasma Devices using 3D-printing of polymeric materials [3] . We also fabricated (and continue to characterize) a relatively- inexpensive self-powered detector on a flexible polymeric substrate [4] . The detector responds to light from the visible part of the spectrum. To enable portability for chemical measurements on-site (i.e., in the field) we often used a smartphone for data acquisition and signal processing, thus enabling a sensor-system to be placed on the Internet of Things (IoT) and potentially, to be employed in Society 5.0 applications [5] . To further facilitate use on-site (i.e., in the field), portable optical spectrometers with a short focal length must be used. But as focal length decreases, spectral overlaps (often called spectral interference effects) arise. To address them, we employed Artificial Intelligence (AI) methods using Artificial Neural Networks (ANNs) and Deep Learning approaches, thus (in many respects) making sensor-systems smarter [6] . In this paper (due to space limitations), emphasis will be will be placed on recent developments.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".