High-performance miniature integrated infrared spectrometers for industrial and biochemical sensing
Bibliographic record
Abstract
High-resolution, miniature integrated spectrometers have been constructed for the NIR and MIR spectral ranges, based on MPBT's proprietary IOSPEC technology. Advanced slab-waveguide integrated optics have been employed to extend the performance of the miniature IR spectrometers to rival that of much larger FT-JR spectrometers. Monolithic integration of the miniature spectrometer, input optics and detector array provides a very compact and robust package that is suitable for industrial and field environments. Despite the compact size of the spectrometers, resolutions of 4 to 8 cm-1 are achievable over dedicated spectral ranges (2000 to 4000 nm, respectively). These spectrometers are coupled to 256channel linear detector arrays controlled by software based on Visual C++ to provide rapid spectral acquisition and analysis. This technology facilitates on-line infrared spectral analysis of an industrial or biochemical process at scan rates exceeding 200 spectra/sec. Since the spectral data is measured directly, significantly less data processing is required than for FT-JR techniques, allowing more CPU time for spectral identification and analysis. Multi-channel, time-resolved spectral measurements permit the study of the intermediate steps in a process or reaction. This paper discusses recent advances in the performance of the miniature integrated spectrometers. New detector geometries and data processing techniques have facilitated a substantial improvement in the overall system SNR over that feasible with typical sequentially-scanned detector arrays. Preliminary experimental transmittance spectra of optical filters and plastics are presented.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 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".