The heterogeneity and electro-mechanical characteristics of coal at the micro- and nanoscale
Bibliographic record
Abstract
Abstract With regard to the mechanism of electromagnetic radiation (EMR) excited by deformation and failure of coal and rock, the present study employed the optical microscope, digital microhardness tester and atomic force microscopy (AFM) to measure surface morphology, surface microhardness and electro-mechanical characteristics, including elastic modulus and surface potential of coal. The results show that micro-mineral composition and micro-mechanical properties of coal are clearly heterogeneous. The elastic modulus values measured are 62.3 MPa–4.0 GPa and the surface potential values tested are 21.2–166.2 mV. The proportion distributions of the two parameters mentioned follow the normal distribution, which indicates the electro-mechanical characteristics of coal are clearly inhomogeneous at the micro- and nanoscales. Finally, the effects of the inhomogeneous elastic modulus and surface potential on the EMR from the microscopic perspective were analyzed. In this experiment, the change of the micro-elastic modulus and the existence of the surface potential on the coal surface were directly observed. The findings reveal the mechanism of EMR induced by deformation and failure of coal and rock.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".