Automating Trace Detection and Chemical Purity Analysis of Carbon Nanotube Mixtures by Non-Negative Matrix Factorization of Spatial Raman Scattering Hyperspectra
Bibliographic record
Abstract
Carbon nanotubes come in different species having different properties. So, it is useful to develop automated ways to quantify species purities and trace impurity content. Spatially scanned Raman spectra make hyperspectral data sets that can be used to discriminate between species and determine purities, and their analysis can be automated. A promising analytical machine learning approach is non-negative matrix factorization, which is a multivariate algorithm well adapted to hyperspectral Raman scattering data sets, and, significantly, available in open source. We prepare samples from different concentrations of pure nanotubes and acquire spatially scanned Raman scattering hyperspectra. We compare the known concentrations of the source dispersions to those determined from hyperspectra acquired from deposited materials on substrates. Here, we demonstrate and deal with several metrological issues: We show that the stability of the focusing conditions is critical. We show that if there are strong peaks that are not significant, normalization is helpful. We show that this approach compares favorably to the “best case” situation where the spectra are factored into a priori known library spectra. Scans of around 100 data points provided good bounds on concentrations down to about the parts per thousand level. Using more than one laser wavelength, so that different species are brought into resonance with each laser should enable higher relative purity measurements. However, there is an important consideration if the difference in laser energies is less than or comparable to the phonon energy. Overall, this approach is promising for the determination of chemical purity of carbon nanotubes and could be generalized to other chemical mixtures.
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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 | 0.001 |
| Science and technology studies | 0.000 | 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".