Advanced Wear-resistant Nanocomposites for Increased Energy Efficiency (Final Technical Report)
Bibliographic record
Abstract
This report summarizes the work performed by an Ames-led project team under a 4-year DOE-ITP sponsored project titled, 'Advanced Wear-resistant Nanocomposites for Increased Energy Efficiency.' The Report serves as the project deliverable for the CPS agreement number 15015. The purpose of this project was to develop and commercialize a family of lightweight, bulk composite materials that are highly resistant to degradation by erosive and abrasive wear. These materials, based on AlMgB{sub 14}, are projected to save over 30 TBtu of energy per year when fully implemented in industrial applications, with the associated environmental benefits of eliminating the burning of 1.5 M tons/yr of coal and averting the release of 4.2 M tons/yr of CO{sub 2} into the air. This program targeted applications in the mining, drilling, machining, and dry erosion applications as key platforms for initial commercialization, which includes some of the most severe wear conditions in industry. Production-scale manufacturing of this technology has begun through a start-up company, NewTech Ceramics (NTC). This project included providing technical support to NTC in order to facilitate cost-effective mass production of the wear-resistant boride components. Resolution of issues related to processing scale-up, reduction in energy intensity during processing, and improving the quality and performance of the composites, without adding to the cost of processing were among the primary technical focus areas of this program. Compositional refinements were also investigated in order to achieve the maximum wear resistance. In addition, synthesis of large-scale, single-phase AlMgB{sub 14} powder was conducted for use as PVD sputtering targets for nanocoating applications.
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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".