Effect of Low Temperatures on the Brittle Fracture of Hazelnut Shell
Bibliographic record
Abstract
Kernels are widely used in the food industry because of their high nutritional value.The nutshell obtained by hulling is used as an absorbent.Currently, hulling is carried out without pre-treatment of the shell.However, lowering the temperature allows for reducing the material's strength.When the temperature of cold brittleness is reached, the shell's strength characteristics are reduced, which decreases the cost of breaking.To determine the tensile strength of hazelnut shells at various temperatures, the authors used the method of compressive testing.Compressive testing of materials was carried out using a PM-MG4 universal hydraulic testing machine.An Evercam 1000-8-M highspeed camera was used to determine the deformation amount.To reveal the material's propensity to brittle fracture, the samples were subjected to dynamic loading on a special installation -a pendulum-type copra.Determining the temperature of cold brittleness allowed for designing highly efficient methods of hulling and its instrumentation.The article presents methods for studying the shell's strength characteristics at various temperatures in the range from 25 to -190℃.The results for compressive strength and impact strength of the shell at different temperatures were given.The range of cold brittleness of the shell was determined.The experimental results showed that a decrease in the temperature of the shell led to a transition from mixed to brittle character of the shell's destruction at a temperature range of -40...-80℃.Lowering the shell's temperature reduced its tensile strength by an average of 25-30%, depending on the size of the nut.The obtained results can be used in the development of new methods and technologies based on them for hulling hazelnuts.The values of the shell's tensile strength can be used in the design and calculation of equipment for breaking.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.002 | 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 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".