PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 24, 2026ACS Sustainable Chemistry & Engineering1 citationsOpen Access

Sustainable 3D-Printed Supports Coated with Zirconium-Based Metal–Organic Frameworks for Picolinic Herbicide Extraction

View Full Paper
AGAlejandro Gil-AparicioJGJana GlatzJDJesús Cases Díaz

Key Points

  • The study aims to develop a sustainable extraction method for picolinic herbicides using MOF-coated 3D-printed supports.
  • Integration of 3D-printed supports made from poly(lactic acid) and wood residues with zirconium-based MOFs.
  • Green in situ synthesis of MOFs and postprinting surface functionalization for immobilization.
  • Application to environmental water samples for herbicide detection.
  • Extraction efficiencies ranged from 73% to 107%.
  • Limits of detection between 0.0016 and 0.0032 μg L–1 were achieved.
  • Maintained good reusability across eight extraction cycles.

Abstract

Metal–organic frameworks (MOFs) offer outstanding potential for pollutant extraction; however, their practical use is often hindered by difficulties in handling and recovery. Herein, we report a sustainable MOF-based extraction platform that integrates 3D-printed supports fabricated from poly(lactic acid) and pine wood residues with zirconium-based MOFs grown via a green in situ synthesis. Postprinting surface functionalization enabled the stable immobilization of UiO-66-derived MOFs, selected for their strong affinity toward picolinic herbicides (PHs). The resulting UiO-66-OH@3D-printed device demonstrated high extraction efficiencies (73–107%), excellent sensitivity (limits of detection of 0.0016–0.0032 μg L–1), and good reusability over eight extraction cycles. The platform was successfully applied to the determination of trace PHs in environmental water samples. This work establishes MOF-functionalized sustainable 3D-printed devices as a robust, cost-effective, and environmentally benign strategy for green water pollutant extraction.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Gil-Aparicio et al. (2026) studied this question.

synapsesocial.com/papers/69eb092b553a5433e34b3c07https://doi.org/10.1021/acssuschemeng.6c01944
Ask AI
Helpful
Bookmark
Share
View Full Paper