Integrated deep eutectic system enrichment and AI-assisted high-throughput visual detection for Hg2+ in environmental samples DOI Creative Commons

Yinni Peng,

Kunze Du, Han Yue

и другие.

Journal of Advanced Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Mercury ion (Hg2+), a prevalent heavy metal, is commonly found in environmental soil and water. Its interaction with sulfhydryl groups proteins or lipids can cause oxidative stress disrupt calcium homeostasis. This leads to severe health issues, including digestive, nervous, immune system damage. Conventional Hg2+ detection methods, such as ICP-MS AAS, require complex procedures bulky instruments, limiting their applicability for real-time, on-site analysis. AI-assisted methods have emerged promising solutions, offering portability rapid capabilities. Deep eutectic solvents (DESs), particularly hydrophobic DESs (HDESs), provide an environmentally friendly alternative enriching detecting metal ions. study aims develop portable, cost-effective, colorimetric sensing platform based on silver nanoparticles deep (AgNPs-HDES) enrichment detection. AgNPs-HDES was synthesized using ethylene glycol containing (AgNPs-EG) the hydrogen bond donor. Electrostatic potential maps (ESP) density functional theory (DFT) were employed elucidate its synthesis mechanisms. Smartphone image acquisition combined YOLOv8-based AI software enabled high-throughput analysis A progressive color change from brownish-yellow colorless observed concentration increased, eliminating hydrophilic interference improving sensitivity. The demonstrated linear range of 1-40 μmol·L-1 (R2 = 0.9889) limit 0.23 μmol·L-1. Recovery rates real samples, lake water, soil, seawater industrial sewage, ranged 90.3 % 123 %. established enables rapid, highly accurate across multiple samples simultaneously. AI-assisted, presents valuable tool monitoring pollutant tracking.

Язык: Английский

Natural deep eutectic solvents (NADES) in drug delivery systems: Characteristics, applications, and future perspectives DOI
Hui Li,

Kaining Yang,

Yumin Yang

и другие.

International Journal of Pharmaceutics, Год журнала: 2025, Номер unknown, С. 125509 - 125509

Опубликована: Март 1, 2025

Язык: Английский

Процитировано

0

Integrated deep eutectic system enrichment and AI-assisted high-throughput visual detection for Hg2+ in environmental samples DOI Creative Commons

Yinni Peng,

Kunze Du, Han Yue

и другие.

Journal of Advanced Research, Год журнала: 2025, Номер unknown

Опубликована: Апрель 1, 2025

Mercury ion (Hg2+), a prevalent heavy metal, is commonly found in environmental soil and water. Its interaction with sulfhydryl groups proteins or lipids can cause oxidative stress disrupt calcium homeostasis. This leads to severe health issues, including digestive, nervous, immune system damage. Conventional Hg2+ detection methods, such as ICP-MS AAS, require complex procedures bulky instruments, limiting their applicability for real-time, on-site analysis. AI-assisted methods have emerged promising solutions, offering portability rapid capabilities. Deep eutectic solvents (DESs), particularly hydrophobic DESs (HDESs), provide an environmentally friendly alternative enriching detecting metal ions. study aims develop portable, cost-effective, colorimetric sensing platform based on silver nanoparticles deep (AgNPs-HDES) enrichment detection. AgNPs-HDES was synthesized using ethylene glycol containing (AgNPs-EG) the hydrogen bond donor. Electrostatic potential maps (ESP) density functional theory (DFT) were employed elucidate its synthesis mechanisms. Smartphone image acquisition combined YOLOv8-based AI software enabled high-throughput analysis A progressive color change from brownish-yellow colorless observed concentration increased, eliminating hydrophilic interference improving sensitivity. The demonstrated linear range of 1-40 μmol·L-1 (R2 = 0.9889) limit 0.23 μmol·L-1. Recovery rates real samples, lake water, soil, seawater industrial sewage, ranged 90.3 % 123 %. established enables rapid, highly accurate across multiple samples simultaneously. AI-assisted, presents valuable tool monitoring pollutant tracking.

Язык: Английский

Процитировано

0