A toolbox for the characterization of biobased waxes
Bibliographic record
Abstract
Waxes are a diverse class of molecules with numerous industrial, commercial, household, medical, and personal applications. Despite the wide variety of applications and chemical structures, most waxes are currently classified entirely by their melting and congealing points. This limits the ability to compare wax performance across the literature and impedes efforts at improving the physical functionality of biobased wax mimetics; many applications require waxes with specific physical properties that cannot be correlated to congealing or melting points alone. A toolbox of procedures to characterize and compare waxes based on not only their melting and congealing points, but also the hardness, yield force, viscosity, and solid fat content is presented. This comprehensive toolbox for the characterization of waxes provides a detailed assessment of their individual functional properties and provides an important tool for holistic comparisons of types of waxes based on their performance. This toolbox is especially useful for analyzing and finding potential applications for the next generation of emerging renewable waxes which are being introduced to the market. Practical applications: An increasing number of lipid derived waxes are entering the market. This toolbox provides a set of reproducible and useful experiments which can be used to analyze and compare the properties of waxes. By focusing on properties relevant to a wide range of wax applications, both end users and suppliers can quickly screen new waxes and determine their applicability for specific functions. This is presently not accomplished using only the ASTM standards of the industry. A wax toolbox containing a series of reproducible methods are presented for determining the melting point, congealing point, hardness, yield force, viscosity, and solid fat content of waxes. This toolbox is especially useful for characterization and comparison of waxes based on their functional performance, and can be used to predict the suitability of waxes for specific applications.
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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 | 0.001 | 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 | 0.001 |
| 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".