High-pressure structural behavior of silicon nanowires is investigated up to approximately 22 GPa using angle dispersive X-ray diffraction measurements. Silicon nanowires transform from the cubic to the beta-tin phase at 7.5-10.5 GPa, to the Imma phase at approximately 14 GPa, and to the primitive hexagonal structure at approximately 16.2 GPa. On complete release of pressure, it transforms to the metastable R8 phase. The observed sequence of phase transitions is the same as that of bulk silicon. Though the X-ray diffraction experiments do not reveal any size effect, the pressure dependence of Raman modes shows that the behavior of nanowires is in between that of the bulk crystal and porous Si.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1166/jnn.2005.109 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Preparation and photoelectric properties of Si:B nanowires with thermal evaporation method.
PLoS One
January 2025
College of Physics and Electronic Engineering, Hainan Normal University, HaiKou, China.
We have successfully prepared a significant number of nanowires from non-toxic silicon sources. Compared to the SiO silicon source used in most other articles, our preparation method is much safer. It provides a simple and harmless new preparation method for the preparation of silicon nanowires.
View Article and Find Full Text PDFUltrasensitive Specific Detection of Anti-influenza A H1N1 Hemagglutinin Monoclonal Antibody Using Silicon Nanowire Field Effect Biosensors.
ACS Appl Bio Mater
January 2025
Graduate School of Science and Technology, Gunma University, 1-5-1 Tenjin-Cho, Kiryu, Gunma 376-8515, Japan.
Rapid and sensitive detection of virus-related antigens and antibodies is crucial for controlling sudden seasonal epidemics and monitoring neutralizing antibody levels after vaccination. However, conventional detection methods still face challenges related to compatibility with rapid, highly sensitive, and compact detection apparatus. In this work, we developed a Si nanowire (SiNW)-based field-effect biosensor by precisely controlling the process conditions to achieve the required electrical properties via complementary metal-oxide-semiconductor (CMOS)-compatible nanofabrication processes.
View Article and Find Full Text PDFAn FPGA-Based SiNW-FET Biosensing System for Real-Time Viral Detection: Hardware Amplification and 1D CNN for Adaptive Noise Reduction.
Sensors (Basel)
January 2025
Department of Computer Science, Faculty of Sciences and Humanities Sciences, Majmaah University, Al Majmaah 11952, Saudi Arabia.
Impedance-based biosensing has emerged as a critical technology for high-sensitivity biomolecular detection, yet traditional approaches often rely on bulky, costly impedance analyzers, limiting their portability and usability in point-of-care applications. Addressing these limitations, this paper proposes an advanced biosensing system integrating a Silicon Nanowire Field-Effect Transistor (SiNW-FET) biosensor with a high-gain amplification circuit and a 1D Convolutional Neural Network (CNN) implemented on FPGA hardware. This attempt combines SiNW-FET biosensing technology with FPGA-implemented deep learning noise reduction, creating a compact system capable of real-time viral detection with minimal computational latency.
View Article and Find Full Text PDFMachine Learning-Based Modeling of pH-Sensitive Silicon Nanowire (SiNW) for Ion Sensitive Field Effect Transistor (ISFET).
Sensors (Basel)
December 2024
CNRS, LAAS, 7 Avenue du Colonel Roche, F-31400 Toulouse, France.
The development of ion-sensitive field-effect transistor (ISFET) sensors based on silicon nanowires (SiNW) has recently seen significant progress, due to their many advantages such as compact size, low cost, robustness and real-time portability. However, little work has been done to predict the performance of SiNW-ISFET sensors. The present study focuses on predicting the performance of the silicon nanowire (SiNW)-based ISFET sensor using four machine learning techniques, namely multilayer perceptron (MLP), nonlinear regression (NLR), support vector regression (SVR) and extra tree regression (ETR).
View Article and Find Full Text PDFA flexible 3D ordered SERS sensor for rapid and reliable detection of pesticide residues in fruits.
Chem Commun (Camb)
January 2025
Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices, School of Information and Optoelectronic Science and Engineering, South China Normal University, Guangzhou 510006, China.
We fabricated flexible, three-dimensional (3D) ordered silicon nanowire (SiNW) arrays decorated with high-density silver nanoparticles (AgNPs) for the sensitive and reproducible detection of pesticide residues. These sensors demonstrated a detection limit of 10 M for methyl parathion (MPT) on curved surfaces.
View Article and Find Full Text PDFWant 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!