Advanced materials and manufacturing research for energy, healthcare and nuclear technologies
Bibliographic record
Abstract
Presentation given by Dr. Ashwath Pazhani, PhD, Assistant Professor (Lecturer) in Mechanical Engineering, University of Cumbria, UK. This presentation highlights the University of Cumbria’s growing leadership in sustainable materials and advanced manufacturing for the hydrogen economy, energy transition, and emerging low-carbon industries. It introduces the research portfolio of the Group for Innovation in Hydrogen Research and Sustainability (iHRS), focusing on the development of fully recyclable graphene-reinforced metal matrix composites (MMCs), hydrogen-compatible materials, and net-zero manufacturing technologies. The talk outlines key challenges facing the sector—including material degradation, hydrogen embrittlement, permeation, fabrication costs, and limited circularity—and presents innovative solutions ranging from energy-efficient additive manufacturing and microwave-assisted processing to multiscale modelling, AI-driven materials design, and life-cycle assessment. The presentation further demonstrates the impact of this work across energy, healthcare, and nuclear applications, emphasizing cost-effective storage solutions, improved safety performance under hydrogen exposure, and up to 30% reductions in energy consumption for hydrogen storage and transfer systems. It outlines ongoing collaborations with industrial and international partners and the strategic alignment with UK Net Zero goals, including future infrastructure projects such as BEAM-HUB and major funding pipelines through EPSRC, Innovate UK, Hydrogen Europe Research, and ERC schemes. Overall, the talk showcases how applied research, workforce development, and cross-sector partnerships can accelerate the safe, scalable, and sustainable adoption of hydrogen technologies in the UK and globally. Some of Cumbria’s key businesses and organisations gathered at the University of Cumbria's Brampton Road campus in Carlisle for this event, hosted by the University's Research and Knowledge Exchange (RKE) colleagues. The event sought to bring together innovators, industry leaders and stakeholders to explore opportunities in manufacturing and technology across the region, and marked an important step in strengthening research and knowledge exchange opportunities between the University of Cumbria and businesses.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.116 | 0.067 |
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".