RamanBot: Versatile high throughput Raman system
Bibliographic record
Abstract
Abstract Raman spectroscopy is a powerful tool for qualitative and quantitative analysis in various scientific and industrial fields. However, the development of multisample automated screening remains relatively unexplored. In this paper, we develop RamanBot, a low-cost, easy-to-assemble, and automated Raman spectroscopy system designed for efficient signal collection from samples stored in different types of containers. For the first time, the proposed device introduces the Cartesian motion system, commonly used in 3D printers, to Raman spectroscopy automation. This is achieved by replacing the extrusion head of a commercially available 3D printer with a novel designed “Raman Head”. The Raman head integrates all the necessary optical components required for in-place sample excitation and signal collection. A multimode fiber is used to deliver the excitation laser to the Raman head, whereas the collected Raman signal is delivered to the spectrometer via a fiber bundle. The motion system is programmed to scan predefined sample arrangements using the standard programming language for computer numerical control (G-code). The effect of movement precision on the Raman signal is studied. The introduced device is used in the quantitative analysis of ethanol and methanol. In addition, RamanBot is used to screen six eggs in their commercial packaging with minimal human intervention. The results show that the system is highly stable and capable of delivering reliable Raman measurements, making it a promising solution for high-throughput Raman spectroscopy applications.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.006 |
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".