Wireless Data Acquisition of Transient Signals for Mobile Spectrometry Applications
Bibliographic record
Abstract
Wireless data acquisition using smartphones or handhelds offers increased mobility, it provides reduced size and weight, it has low electrical power requirements, and (in some cases) it has an ability to access the internet. Thus, it is well suited for mobile spectrometry applications using miniaturized, field-portable spectrometers, or detectors for chemical analysis in the field (i.e., on-site). There are four main wireless communications standards that can be used for wireless data acquisition, namely ZigBee, Bluetooth, Wi-Fi, and UWB (ultra-wide band). These are briefly reviewed and are evaluated for applicability to data acquisition of transient signals (i.e., time-domain) in the field (i.e., on-site) from a miniaturized, field-portable photomultiplier tube detector and from a photodiode array detector installed in a miniaturized, field-portable fiber optic spectrometer. These are two of the most widely used detectors for optical measurements in the ultraviolet-visible range of the spectrum. A miniaturized, 3D-printed, battery-operated microplasma-on-a-chip was used for generation of transient optical emission signals. Elemental analysis from liquid microsamples, a microplasma, and a handheld or a smartphone will be used as examples. Development and potential applicability of wireless data acquisition of transient optical emission signals for taking part of the lab to the sample types of mobile, field-portable spectrometry applications will be discussed. The examples presented are drawn from past and ongoing work in the authors' laboratory. A handheld or a smartphone were used as the mobile computing devices of choice.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".