Nanomaterials by severe plastic deformation: review of historical developments and recent advances
Bibliographic record
Abstract
IMPACT STATEMENT This article comprehensively reviews recent advances on development of ultrafine-grained and nanostructured materials by severe plastic deformation and provides a brief history regarding the progress of this field.Severe plastic deformation (SPD) is effective in producing bulk ultrafine-grained and nanostructured materials with large densities of lattice defects. This field, also known as NanoSPD, experienced a significant progress within the past two decades. Beside classic SPD methods such as high-pressure torsion, equal-channel angular pressing, accumulative roll-bonding, twist extrusion, and multi-directional forging, various continuous techniques were introduced to produce upscaled samples. Moreover, numerous alloys, glasses, semiconductors, ceramics, polymers, and their composites were processed. The SPD methods were used to synthesize new materials or to stabilize metastable phases with advanced mechanical and functional properties. High strength combined with high ductility, low/room-temperature superplasticity, creep resistance, hydrogen storage, photocatalytic hydrogen production, photocatalytic CO2 conversion, superconductivity, thermoelectric performance, radiation resistance, corrosion resistance, and biocompatibility are some highlighted properties of SPD-processed materials. This article reviews recent advances in the NanoSPD field and provides a brief history regarding its progress from the ancient times to modernity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".