Corn Zein Nanocarriers for Agrochemical Delivery and Smart Packaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Zein, a prolamin protein derived from corn, has emerged as a multifunctional and scalable nanocarrier for addressing critical sustainability challenges in agriculture and packaging. This review critically synthesizes and evaluates recent advances in zein‐based nanocarriers for targeted delivery of pesticides, herbicides, fungicides, micronutrients, and in smart packaging systems. Drawing from biomedical nanomedicine, it highlights innovations in stimuli‐responsive release of bioactives, hybridization of zein with natural polymers, and plant translocation. Zein, with a Generally Recognized as Safe (GRAS) status, biodegradability, and ability to form stable nanoparticles, positions it as a low‐toxicity alternative to conventional synthetic agrochemical carriers and petroleum‐based plastics. Applications in postharvest preservation, humidity‐triggered release, and biosensor‐enabled packaging further elucidates its multifunctionality. Despite strong lab‐scale performance, major challenges remain in field‐scale agrochemical delivery, gaps in its environmental fate, and regulatory approval. This critical review outlines strategies for overcoming these barriers. Overall, zein nanotechnology, framed within the broader goals of climate‐resilient, resource‐efficient, and circular agriculture, offers a roadmap for leveraging zein nanotechnology to reduce environmental burden while improving agricultural productivity. In addition, the challenges that this review paper addresses strongly promote several United Nations Sustainability Development Goals, including UN SDG2 (zero hunger), SDG 12 (responsible consumption and production), SDG 13 (Climate action), and SDG 15 (life on land).
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it