Determination of Volume Fractions in Multicomponent Mixtures Using Ultrasound Frequency Analysis
Bibliographic record
Abstract
Controlling the composition of mixtures is critical for quality control in a wide variety of applications. There is a need for rapid, on-site measurements to optimize processes in real time. Ultrasound easily penetrates opaque samples and containers, yet currently provides minimal chemical information. We have developed a general approach to determine the volume fraction of a liquid in mixtures with multiple components. Ultrasound waves propagating through a medium undergo distortion processes that are characteristic of the chemical bonding composition. The distortion of the waveform can be measured in the ultrasound frequency profile. An ultrasound pulse-through configuration with matching 5 MHz transducers was used to analyze mixtures of water, methanol, and ethanol. Multilinear regression analysis was used to determine the volume fraction of all components in a series of mixtures. Using this technique, volume fractions were determined simultaneously with correlation coefficients (r(2)) greater than 0.98 in two-component mixtures. Determination of volume fractions in three-component mixtures ranging from 65-100% water also showed correlation coefficients of 0.91 for methanol and 0.94 for ethanol. This technique is attractive for process monitoring due to the short measurement time and the simple methodology that excludes sample pretreatment.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".