Development and Characterization of Lignin‐Based Hydrogel for Use in Agricultural Soils: Preliminary Evidence
Bibliographic record
Abstract
Abstract In arid and semi‐arid regions of the world, agricultural production is greatly limited by water scarcity and inefficient water use. Water‐absorbent hydrogels are a technological solution that can retain soil water for plants. A lignin‐based hydrogel as a natural plant‐based water absorbent is prepared from lignin alkali polymers and poly(ethylene glycol) diglycidyl ether (PEGDGE) in adjusted alkali (NaOH) solution. The maximum swelling capacity of the hydrogel is achieved in 1.5 m NaOH with 0.5 mmol PEGDGE g. Water swelling capacity is 34 g g dry weight of hydrogel in distilled water, which is reduced to 53% and 64% in 0.1 m NaCl and 0.1 m CaCl 2 solution, respectively. Biodegradability and phytotoxicity tests show that 6.5% of the sample mass decomposed after 40 days of incubation in soil solution media and the hydrogel is not phytotoxic to wheat seeds. These findings support the use of the lignin‐based hydrogel as an environmentally friendly additive to promote water retention in dry, saline soils. Due to the limitations of this study, further assessments are needed in order to understand the efficiency of lignin‐based hydrogel application in different soils with different biota.
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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".