Controlling copper ion detection by modulating the deacetylation degree of chitosan in carboxymethyl cellulose/chitosan-based sensors

In this study, we report sensors with interdigitated electrodes functionalized with layer-by-layer (LbL) films of carboxymethyl cellulose (CMC) and chitosan (CHI) for copper ion detection. Variations in CMC/CHI bilayers (1, 3, and 5) and the degree of deacetylation (DD ≈ 95%, 75%, 55%) of high molecular weight chitosans (approx. 10<sup>6</sup> g/mol) were explored for their impact on electrode sensitivity using anodic stripping voltammetry. Sensors made with 95% deacetylated chitosan exhibited nearly double the current intensity compared to those prepared with lower DD, highlighting the crucial role of amino groups in sensitivity. Rougher films enhanced copper detection, and optimal performance was achieved with three bilayers, demonstrating excellent reproducibility (2% standard deviation). The linear detection range was from 0.5 to 2.5 ppm, with a detection limit of 0.05 ppm. Validation with another analytical technique revealed relative errors ranging from 2.1% to 4.6%, confirming the method’s accuracy. The enhancement mechanism is linked to the physical-chemical characteristics of the CMC/CHI films, where the amino groups of CHI assist in copper ion complexation, improving sensor effectiveness. The control of chitosan’s DD provides a new level of customization in the properties of LbL films, enhancing the sensor’s analytical capability for highly sensitive metal ion detection.

Publication Details

Journal
Figshare
Published
2025-10-14
DOI
https://doi.org/10.6084/m9.figshare.30353268
Primary Topic
Electrochemical Analysis and Applications
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Controlling copper ion detection by modulating the deacetylation degree of chitosan in carboxymethyl cellulose/chitosan-based sensors

Figshare
Electrochemical Analysis and Applications
article

Controlling copper ion detection by modulating the deacetylation degree of chitosan in carboxymethyl cellulose/chitosan-based sensors

article en

Abstract

In this study, we report sensors with interdigitated electrodes functionalized with layer-by-layer (LbL) films of carboxymethyl cellulose (CMC) and chitosan (CHI) for copper ion detection. Variations in CMC/CHI bilayers (1, 3, and 5) and the degree of deacetylation (DD ≈ 95%, 75%, 55%) of high molecular weight chitosans (approx. 10<sup>6</sup> g/mol) were explored for their impact on electrode sensitivity using anodic stripping voltammetry. Sensors made with 95% deacetylated chitosan exhibited nearly double the current intensity compared to those prepared with lower DD, highlighting the crucial role of amino groups in sensitivity. Rougher films enhanced copper detection, and optimal performance was achieved with three bilayers, demonstrating excellent reproducibility (2% standard deviation). The linear detection range was from 0.5 to 2.5 ppm, with a detection limit of 0.05 ppm. Validation with another analytical technique revealed relative errors ranging from 2.1% to 4.6%, confirming the method’s accuracy. The enhancement mechanism is linked to the physical-chemical characteristics of the CMC/CHI films, where the amino groups of CHI assist in copper ion complexation, improving sensor effectiveness. The control of chitosan’s DD provides a new level of customization in the properties of LbL films, enhancing the sensor’s analytical capability for highly sensitive metal ion detection.

Figshare
Openalex Percentile: Top 56%
Electrochemical Analysis and Applications
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.