Life Cycle Assessment of Piezoelectric Materials Used for Energy Harvesting Systems: PZT Versus KNN
Bibliographic record
Abstract
Over the past decade, piezoelectric materials have been extensively used in various engineering areas, particularly for energy harvesting, due to their efficient electromechanical conversion. Several scientific researches reported in the literature have performed modeling, prototyping, and experimentation to improve the performance of harvesters. Piezoelectric transducers, especially those used for energy harvesting, could have significant environmental impacts. Few studies have emphasized the environmental issue of harvesters in their life cycle. This paper aims to study the environmental impacts of piezoelectric energy harvesters obtained with screen-printing process by performing a life cycle assessment (LCA). Firstly, a comprehensive overview of piezoelectric materials and their implementation in the context of micro electromechanical systems is first presented. Secondly, the environmental impacts of these materials are briefly discussed based on previous studies. Here the aim is to present a comparative analysis of the environmental consequences of energy harvesters based on different piezoelectric materials, from cradle to gate. The harvester based on lead zirconate titanate Pb(Zr,Ti)O3 (PZT) is taken as a reference. The impact assessment is conducted by evaluating the selected impact categories using ILCD 2011 method. Results highlight the most impactful components of energy harvesters referring to the unique scores calculated for different impact categories at the Midpoint. They highlight also the importance of LCA and offer technical guidance and crucial recommendations for eco-design.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".