The 6th International Workshop on Micro and Nanotechnologies for Power Generation and Energy Conversion Applications (PowerMEMS 2006)
Bibliographic record
Abstract
Energy is a sector of paramount importance over the coming decades if we are to ensure sustainable development that respects our environment. The research and development of novel approaches to convert available energy into usable forms using micro and nanotechnologies can contribute towards this goal and meet the growing need for power in small scale portable applications. The dominant power sources for handheld and other portable electronics are currently primary and rechargeable batteries. Their limited energy density and adverse effects on the environment upon disposal suggest that alternative approaches need to be explored. This special issue will showcase some of the leading work in this area, initially presented at PowerMEMS 2006, the 6th International Workshop on Micro and Nanotechnologies for Power Generation and Energy Conversion Applications. Power MEMS are defined as microsystems for electrical power generation and other energy conversion applications, including propulsion and cooling. The range of power MEMS technologies includes micro thermodynamic machines, such as microturbines, miniature internal combustion engines and micro-coolers; solid-state direct energy conversion, such as thermoelectric and photovoltaic microstructures; micro electrochemical devices, such as micro fuel cells and nanostructure batteries; vibration energy harvesting devices, such as piezoelectric, magnetic or electrostatic micro generators, as well as micro thrusters and rocket engines for propulsion. These can either be driven by scavenging thermal, mechanical or solar energy from the environment, or from a stored energy source, such as chemical fuel or radioactive material. The unique scope leads to unique challenges in the development of power MEMS, ranging from the integration of novel materials to the efficient small scale implementation of energy conversion principles. In this special issue, Mitcheson et al provide a comparative assessment of three inertial vibration energy harvesting approaches. Technologies and approaches for micro heat engines are shared, ranging from a complete microsystem for thermal energy harvesting (Cho et al ) to core bearing and microturbomachinery technologies for rotating micro heat engines (Waits et al , Nakajima et al ). Electrochemical microsystems are also presented, based on methanol as fuel (Morse et al ), as well as novel micro and nanofabrication approaches (Chu et al ). Fuel cell microsystems with integrated hydrogen generation approaches are also investigated by Peterson et al and Varady et al , illustrating the benefits and challenges of miniaturizing complete power sources. Finally, biological micro fuel cells that leverage the principles found in nature are presented, in contrast to chemical fuel cells (Chen et al , Morishima et al ). We hope that this work will inspire others to pursue innovative research and development activities in the area of power MEMS, and consequently contribute to addressing our energy challenges for the 21st century.
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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 | 0.002 | 0.002 |
| 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 | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.072 | 0.037 |
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".