3D printed topologically engineered cellulose nanofiber/carbon nanotube scaffolds for highly sensitive humidity sensing

Humidity sensors have attracted considerable attention for their wide applications in industrial fields and daily life, yet the fabrication of high-performance sensors still remains challenging. In this study, we developed a highly sensitive humidity sensor based on a topologically engineered cellulose nanofiber/carbon nanotube (CNF/CNT) composite scaffold fabricated by 3D printing combined with freeze-drying. The hierarchical porous architecture, featuring millimeter-sized topological architectures generated by 3D printing and micrometer-sized pores introduced by freeze-drying, provides abundant active interfaces for water molecule adsorption and enables tailored conductive topology toward the percolation threshold. Enabled by this rational topological design, the optimized scaffold sensor exhibits a high humidity sensitivity of 526.4% (ΔR/R 0 , sampling time: 5 min) at 95% relative humidity (RH), with a wide operating humidity range from 11% to 95% RH and excellent long-term stability over two months. Compared to previously reported cellulose/CNT-based humidity sensors, our 3D printed scaffold sensor demonstrates one of the highest sensitivities. Furthermore, the sensor shows promising potential in practical applications including human respiration monitoring, voice recognition, and non-contact fingertip humidity detection. This work provides a scalable and controllable strategy for designing topologically engineered, high-performance humidity sensors using sustainable cellulose-based materials.

Authors

Institutions

Publication Details

Journal
Industrial Crops and Products
Published
2026-10-31
DOI
https://doi.org/10.1016/j.indcrop.2026.124633
Primary Topic
Gas Sensing Nanomaterials and Sensors
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

3D printed topologically engineered cellulose nanofiber/carbon nanotube scaffolds for highly sensitive humidity sensing

Gang Chen, Feng Jiang, Ruolin Wang, Penghui Zhu
Industrial Crops and Products
Gas Sensing Nanomaterials and Sensors
article

3D printed topologically engineered cellulose nanofiber/carbon nanotube scaffolds for highly sensitive humidity sensing

Gang Chen, Feng Jiang, Ruolin Wang, Penghui Zhu
article en

Abstract

Humidity sensors have attracted considerable attention for their wide applications in industrial fields and daily life, yet the fabrication of high-performance sensors still remains challenging. In this study, we developed a highly sensitive humidity sensor based on a topologically engineered cellulose nanofiber/carbon nanotube (CNF/CNT) composite scaffold fabricated by 3D printing combined with freeze-drying. The hierarchical porous architecture, featuring millimeter-sized topological architectures generated by 3D printing and micrometer-sized pores introduced by freeze-drying, provides abundant active interfaces for water molecule adsorption and enables tailored conductive topology toward the percolation threshold. Enabled by this rational topological design, the optimized scaffold sensor exhibits a high humidity sensitivity of 526.4% (ΔR/R 0 , sampling time: 5 min) at 95% relative humidity (RH), with a wide operating humidity range from 11% to 95% RH and excellent long-term stability over two months. Compared to previously reported cellulose/CNT-based humidity sensors, our 3D printed scaffold sensor demonstrates one of the highest sensitivities. Furthermore, the sensor shows promising potential in practical applications including human respiration monitoring, voice recognition, and non-contact fingertip humidity detection. This work provides a scalable and controllable strategy for designing topologically engineered, high-performance humidity sensors using sustainable cellulose-based materials.

Industrial Crops and ProductsVol. 253
University of British Columbia (CA), Nanjing Forestry University (CN), BioProducts Institute (CA), South China University of Technology (CN)
Openalex Percentile: Top 24%
Gas Sensing Nanomaterials and Sensors
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.

Laika access coming soon