Survey of energy harvesting and energy scavenging approaches for<i>on-site</i>powering of wireless sensor- and microinstrument-networks
Bibliographic record
Abstract
Energy (or power) harvesting can be defined as the gathering and either storing or immediately using energy “freely” available in a local environment. Examples include harvesting energy from obvious sources such as photon-fluxes (e.g., solar), or wind or water waves, or from unusual sources such as naturally occurring pH differences. Energy scavenging can be defined as gathering and storing or immediately re-using energy that has been discarded, for instance, waste heat from air conditioning units, from in-door lights or from everyday actions such as walking or from body-heat. Although the power levels that can be harvested or scavenged are typically low (e.g., from nWatt/cm<sup>2</sup> to mWatt/cm<sup>2</sup>), the key motivation is to harvest or to scavenge energy for a wide variety of applications. Example applications include powering devices in remote weather stations, or wireless Bluetooth headsets, or wearable computing devices or for sensor networks for health and bio-medical applications. Beyond sensors and sensor networks, there is a need to power compete systems, such as portable and energy-autonomous chemical analysis microinstruments for use <i>on-site</i>. A portable microinstrument is one that offers the same functionality as a large one but one that has at least one critical component in the micrometer regime. This paper surveys continuous or discontinuous energy harvesting and energy scavenging approaches (with particular emphasis on sensor and microinstrument networks) and it discusses current trends. It also briefly explores potential future directions, for example, for nature-inspired (e.g., photosynthesis), for human-power driven (e.g., for biomedical applications, or for wearable sensor networks) or for nanotechnology-enabled energy harvesting and energy scavenging approaches.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".