Development of data acquisition and analysis methods for chemical acoustic emission
Bibliographic record
Abstract
Acoustic Emission Analysis (AEA) is the study of the sonic (and ultrasonic) energy released by chemical systems in the form of transient waves, as the system attempts to (re)attain equilibrium. This area of chemistry, and chemical analysis, is ripe for fundamental studies since it has been little explored. The high potential of the technique as a non-invasive, non-destructive reaction monitoring scheme suggests that numerous applications will follow. In this work, an apparatus and software have been constructed to monitor acoustic emission (AE) and collect and process AE data. A broad-band piezoelectric transducer was used to convert the acoustic signals to electrical waveforms which could be captured by a digital storage oscilloscope. These waveforms were then stored on an IBM-compatible computer for further analysis. Analysis of the data was performed using pattern recognition techniques. The signals were characterized through the use of descriptors which can map each signal onto a multi-dimensional feature space. Visualization of the data structure in multidimensional space was accomplished using several methods. Hierarchical clustering was used to produce tree structures, known as dendrograms, which attempt to show clustering of the signals into various groups. Abstract factor analysis (AFA) - also called principal components analysis (PCA) - was used to project the data onto a two dimensional factor space to allow for direct viewing of structure in the multidimensional data. Sodium hydroxide dissolution, aluminum chloride hydration and heat activation of Intumescent Flame Retardants (IFR's) were used to test the assembled hardware and to provide data to submit to the pattern recognition algorithms coded as part of this work. The solid-solid phase transition of trimethylolethane (Trimet), and the liquid crystal phase transitions of two liquid crystals (α-ѡ-bis(4-n-decylaniline-benzilidene-4'-oxyhexane), and 4-n-pentyloxybenzylidene-4'-n-heptylaniline) were also monitored and the signals analyzed. The pattern recognition software was able to extract much information from the acoustically emitting samples - information which would not have been apparent by using standard (uni- and bi-variate) methods of analysis. Chemical acoustic emission, coupled with pattern recognition analysis, will be able to provide the chemist with knowledge (qualitative, quantitative, kinetic, etc.) about chemical systems which are often difficult or impossible to monitor and analyze by other means.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".