Sustainable Advanced Manufacturing of Printed Electronics: An Environmental Consideration
Bibliographic record
Abstract
Printing technologies have become a novel and disruptive innovation method of manufacturing electronic components to produce a diverse range of devices including photovoltaic cells, solar panels, energy harvesters, batteries, light sources, and sensors on really thin, lightweight, and flexible substrates. In traditional electronic manufacturing, a functional layer must be deposited, typically through a chemical vapor or physical vapor process for a copper layer for circuitry production. These subtractive techniques involve multiple production steps and use toxic etching chemicals to remove unwanted photoresist layers and metals. In printing, the same functional material can be selectively deposited only where it is needed on the substrate via plates or print heads. The process is additive and significantly reduces not only the number of manufacturing steps, but also the need for energy, time, consumables, as well as the waste. Thereby, printing has been in the focus for many applications as a green, efficient, energy-saving, environmentally friendly manufacturing method. This chapter presents a general vision on green energy resources and then details printed electronics that consolidates green energy and environment relative to traditional manufacturing system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.013 |
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".