Editorial: Focus on green nanomaterials for a sustainable internet of things
Bibliographic record
Abstract
Abstract In the dynamic landscape of the Internet of Things (IoT), where smart devices are reshaping our world, nanomaterials can play a pivotal role in ensuring the IoT’s sustainability. These materials are poised to redefine the development of smart devices, not only enabling cost-effective fabrication but also unlocking novel functionalities. As the IoT is set to encompass an astounding number of interconnected devices, the demand for environmentally friendly nanomaterials takes center stage. This Focus Issue spotlights cutting-edge research that explores the intersection of nanomaterials and sustainability. The collection delves deep into this critical nexus, encompassing a wide range of topics, from fundamental properties to applications in devices (e.g. sensors, optoelectronic synapses, energy harvesters, memory components, energy storage devices, and batteries), aspects concerning circularity and green synthesis, and an array of materials comprising organic semiconductors, perovskites, quantum dots, nanocellulose, graphene, and two-dimensional semiconductors. Authors not only showcase advancements but also delve into the sustainability profile of these materials, fostering a responsible endeavour toward a green IoT future.
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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.020 |
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".