Semiconducting nanomaterials with 3D network structures exhibit various fascinating properties such as electrical conduction, high permeability, and large surface areas, which are beneficial for adsorption, separation, and sensing applications. However, research on these materials is substantially restricted by the limited trans-scalability of their structural design and tunability of electrical conductivity. To overcome this challenge, a pyrolyzed cellulose nanofiber paper (CNP) semiconductor with a 3D network structure is proposed. Its nano-micro-macro trans-scale structural design is achieved by a combination of iodine-mediated morphology-retaining pyrolysis with spatially controlled drying of a cellulose nanofiber dispersion and paper-crafting techniques, such as microembossing, , and . The electrical conduction of this semiconductor is widely and systematically tuned, the temperature-controlled progressive pyrolysis of CNP, from insulating (10 Ω cm) to quasimetallic (10 Ω cm), which considerably exceeds that attained in other previously reported nanomaterials with 3D networks. The pyrolyzed CNP semiconductor provides not only the tailorable functionality for applications ranging from water-vapor-selective sensors to enzymatic biofuel cell electrodes but also the designability of macroscopic device configurations for stretchable and wearable applications. This study provides a pathway to realize structurally and functionally designable semiconducting nanomaterials and all-nanocellulose semiconducting technology for diverse electronics.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9245344PMC
http://dx.doi.org/10.1021/acsnano.1c10728DOI Listing

Publication Analysis

Top Keywords

semiconductor network
8
network structure
8
nano-micro-macro trans-scale
8
semiconducting nanomaterials
8
electrical conduction
8
structural design
8
cellulose nanofiber
8
cnp semiconductor
8
nanocellulose paper
4
semiconductor
4

Similar Publications

Printed circuit boards represent an extraordinarily challenging fraction for the recycling of waste electric and electronic equipment. Due to the closely interlinked structure of the composing materials, the selective recycling of copper and closely associated precious metals from this composite material is compromised by losses during mechanical pre-processing. This problem could partially be overcome by a better understanding of the influence of particle size and shape on the recovery of finely comminuted and well-liberated metal particles during mechanical separation.

View Article and Find Full Text PDF

Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.

Sci Rep

January 2025

Department of Biomedical Engineering, School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.

Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established.

View Article and Find Full Text PDF

Inorganic plastic semiconductors play a crucial role in the realm of flexible electronics. In this study, we present a cost-effective plastic thermoelectric semimetal magnesium bismuthide (α-MgBi), exhibiting remarkable thermoelectric performance. Bulk single-crystalline α-MgBi exhibits considerable plastic deformation at room temperature, allowing for the fabrication of intricate shapes such as the letters "SUSTECH" and a flexible chain.

View Article and Find Full Text PDF

Photoinduced hidden monoclinic metallic phase of VO driven by local nucleation.

Nat Commun

January 2025

State Key Laboratory of Superlattices and Microstructures, Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China.

The insulator-to-metal transition in VO has garnered extensive attention for its potential applications in ultrafast switches, neuronal network architectures, and storage technologies. However, the photoinduced insulator-to-metal transition remains controversial, especially whether a complete structural transformation from the monoclinic to rutile phase is necessary. Here we employ the real-time time-dependent density functional theory to track the dynamic evolution of atomic and electronic structures in photoexcited VO, revealing the emergence of a long-lived monoclinic metal phase under low electronic excitation.

View Article and Find Full Text PDF

Ultra robust negative differential resistance memristor for hardware neuron circuit implementation.

Nat Commun

January 2025

Key Laboratory of Brain like Neuromorphic Devices and Systems of Hebei Province, College of Physics Science and Technology, Hebei University, Baoding, Hebei, China.

Neuromorphic computing holds immense promise for developing highly efficient computational approaches. Memristor-based artificial neurons, known for due to their straightforward structure, high energy efficiency, and superior scalability, which enable them to successfully mimic biological neurons with electrical devices. However, the reliability of memristors has always been a major obstacle in neuromorphic computing.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!