Evidence map›Paper›PMID 41002456›Full record

ReviewGels (Basel, Switzerland)2025

Stimuli-Responsive Starch-Based Biopolymer Coatings for Smart and Sustainable Fertilizers.

Babar Azeem

Abstract readReview
In one paragraph

Review in Gels (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Article
  2. Review
  3. Review
  4. Review
  5. Article
  6. Article
  7. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Babar AzeemDepartment of Chemical Engineering, College of Engineering, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia.

Funding

Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University IMSIU-DDRSP2502
6 · The paper itself

Abstract

The quest for sustainable agriculture demands nutrient delivery systems that align productivity with environmental responsibility. This review critically evaluates stimuli-responsive starch-based biopolymer coatings for controlled-release fertilizers (CRFs), highlighting their structure, functionality, and agronomic relevance. Starch, an abundant and biodegradable polysaccharide, offers intrinsic advantages such as modifiability, film-forming ability, and compatibility with green chemistry. The paper discusses starch's physicochemical characteristics, its functionalization to achieve responsiveness to environmental triggers (pH, moisture, temperature, ionic strength), and coating strategies like in situ polymerization, grafting, and nanocomposite integration. A comprehensive analysis of release kinetics, swelling behavior, biodegradability, and water retention is provided, followed by evaluations under simulated field conditions, encompassing various soil types, environmental stressors, and crop responses. Comparative insights with other smart biopolymers such as chitosan, alginate, and cellulose underscore starch's unique position in CRF technology. Despite promising developments, the review identifies critical research gaps, including limitations in scalability, coordination of multi-stimuli responses, and the need for extensive field validation. This work serves as a consolidated platform for researchers, policy makers, and agro-industrial stakeholders aiming to design smart, eco-friendly fertilizers that address global food security while minimizing ecological footprints.

Indexed as

biodegradable coatingscontrolled-release fertilizerssmart nutrient deliverystarch-based biopolymersstimuli-responsive polymerssustainable agro-technologies

Identifiers

PMID41002456
PMCPMC12469964

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.