MétaCan
Menu
Back to cohort
Record W1986692381 · doi:10.1515/epoly.2009.9.1.1718

Release behavior of 2,4-dichlorophenoxyacetic acid herbicide using novel porous polyacrylamide hydrogels

2009· article· en· W1986692381 on OpenAlexaff
Gholam Reza Mahdavinia, Seyed Bahman Mousavi, Farrokh Karimi, Gholam Bagheri Marandi, Saleh Shahabivand, Mohammad Harati

Bibliographic record

Venuee-Polymers · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsWestern University
FundersUniversity of Maragheh
KeywordsAmmonium persulfateSelf-healing hydrogelsAcrylamidePolyacrylamidePolymerizationSodium metabisulfiteChemistryMonomerChemical engineeringSwellingPorosityPolymer chemistryNuclear chemistryPolymerMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Porous polyacrylamide (PAAm) was synthesized using calcium carbonate (CCb) micro-powder and subsequent leaching process. Polymerization was carried out in a solution environment system containing methylenebis acrylamide (MBA) and acrylamide (AAm) as crosslinker and monomer, respectively. CCb micro-powders were introduced to the polymerization solution before adding initiator. Ammonium persulfate/sodium metabisulfite (APS/SMBS) were used as a redox initiator system. HCl solution was used to remove CCb powders. The effect of MBA concentration and CCB amount on the swelling of hydrogels were studied. The porous structure of hydrogels was investigated and verified using scanning electron microscopy (SEM). 2,4-Dichlorophenoxyacetic acid (2,4-D) was loaded into the hydrogels and release of this herbicide was investigated. The effect of MBA concentration and addition of CCb on the content of released herbicide was studied. Also, the study of herbicide release in a solution under various pH revealed the pH-dependency release of 2,4-D herbicide.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.001

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.240
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuee-PolymersSame topicPesticide and Herbicide Environmental StudiesFrench-language works237,207