The Introduction of Thin Open-Cell Metal Foams and their Wider Engineering Applications
Bibliographic record
Abstract
Metal foams, having a great of specific surface areas and three-dimensional open cell structures, can be used for electromagnetic shielding and thermal management in electronics, battery electrodes in new energy, catalyst carriers in chemical engineering and lightweight structure in aerospace. The key performance indicators of these open cell metal foams are how to effectively control their pore structures such as pore diameter distribution and porosity and how to make it thinner that can meet different requirements for the component preparation. Summarized about three manufacturing processes are usually used to build metal foams. The first is well-known as physical process. The second is chemical or electrochemical method. The third is a combined process between physical and chemical processes. No matter what kind of process is selected to manufacture open cell metal foam, the specific surface area directly related to the microstructure of the foam is an important parameter in a material selection and design for its application.This paper will introduce a special powder composite plus manufacture process that is developed by Jiangsu Green Materials Hi-Tech. Co. Ltd. The leading manufacture process is a new one that is combined by powder metallurgy, casting, deformation and physical-chemical synthesis. It is environmentally friendly and recyclable from raw material selection, manufacturing process to thermal-mechanical treatment. This paper will also focus on the introduction of the microstructure characterization of open-cell metal foams such as copper, nickel, iron, silver and their alloying foams that we manufactured and give some examples to demonstrate their potential applications in the field of new energy, such as being an electrode for lithium-ion battery, membranes for fuel cell and super-capacitor, in the field of electronic engineering such as thermal management and electromagnetic shielding, in the field of chemical engineering such as separation and catalysts. These examples show their lead roles of these open-cell metal foams and different applications by our developed process.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".