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Plasmon-enhanced ultra-high photoresponse of single-wall carbon nanotube/copper/silicon near-infrared photodetectors
Nano Research
Published: 01 April 2024
Downloads:45

Single wall carbon nanotube (SWCNT)/Si heterojunction photodetectors have the advantages of high photoresponse ability and simple structure, however, their detection wavelength range are usually lower than 1100 nm, which limits their application in the infrared band. We report a SWCNT/Cu/Si photodetector with both a high photoresponse and a detection range up to the infrared band by depositing a Cu nanoparticles (NPs) layer between a SWCNT film and a n-Si substrate. It was found that the Cu NPs produce strong surface plasmon resonance (SPR) under laser irradiation, which breaks through the limitation of Si band gap and greatly improves the photoresponse of the SWCNT/Cu/Si photodetector in the near infrared band. The responsivity (R) of the photodetector in the wavelength range of 1850–1200 nm reached 2.2–14.15 mA/W, which is the highest value in the reported plasmon enhanced n-Si based photodetectors, and about 20,000 times higher than that of a SWCNT/Si photodetector. Its R value for 1550 nm wavelength used in optical communications reached ~ 8.2 mA/W, which is 64% higher than the previously reported values of commonly used photodetectors. We attribute the significant increase to the strong SPR and low Schottky barrier of Cu with n-Si, which facilitates the generation and transfer of the carriers.

Research Article Issue
Single-wall carbon nanotube fiber non-woven fabrics with a high electrothermal heating response
Nano Research 2024, 17 (6): 5621-5628
Published: 15 January 2024
Downloads:34

Carbon nanotube (CNT) fibers have great promise for constructing multifunctional fabrics with high electrical conductivity, good electro-heating ability, excellent flexibility, and a low density. However, the inter-fiber contacts in the fabric greatly reduce these advantages and limit their application. Herein, a simple pressure-fusing method to fabricate single-wall CNT (SWCNT) fiber non-woven fabrics (NWFs) that are composed of interconnected SWCNT fibers with fused joints is reported, which have good flexibility, a low density of 0.46 g/cm3, a high electrical conductivity of 3.7 × 105 S/m, and a record high specific electrical conductivity of 803 (S·m2)/kg. They also showed excellent electrical heating ability, so that a temperature of ~ 160 °C was rapidly reached at a low voltage of 2 V. Combined with their low density, the SWCNT fiber NWFs are promising for use as a heating unit for low temperature battery protection and de-icing applications.

Research Article Issue
Correlating the fluctuated growth of carbon nanotubes with catalyst evolution by atmospheric-pressure environmental transmission electron microscopy
Nano Research 2023, 16 (11): 12781-12787
Published: 28 October 2023
Downloads:96

Rate-controlled growth of carbon nanotubes (CNTs) and catalyst design are considered efficient ways for the preparation of CNTs with specific structures and properties. However, due to the difficulties in capturing the growth process of the CNTs with tiny size under a complex growth environment, the growth kinetics of CNTs and their correlation with the catalyst seed have been seldom revealed. Here, we investigated the growth process of CNTs from Ni nanoparticles (NPs) in real-time under atmospheric pressure using transmission electron microscopy equipped with a closed gas cell. It was found that the growth rates of CNTs fluctuated, and a phase transition from Ni3C to Ni, and a reshaping of the catalyst NPs occurred during the growth process. We demonstrated that CNTs dynamically interacted with the connected catalyst NPs and the fluctuated growth rates of CNTs were correlated with the structure change of catalyst NPs. The origin of the growth rate fluctuation is attributed to the change of carbon concentration gradient in catalyst NPs.

Erratum Issue
Erratum to: Tannic acid coated single-wall carbon nanotube membranes for the recovery of Au from trace-level solutions
Nano Research 2024, 17 (4): 3426
Published: 04 October 2023
Downloads:10
Research Article Issue
Tannic acid coated single-wall carbon nanotube membranes for the recovery of Au from trace-level solutions
Nano Research 2023, 16 (8): 11350-11357
Published: 27 June 2023
Downloads:84

The efficient recovery of gold from industrial sewage is important for saving precious metals and remains a big challenge. We report the extraction of gold ions from a trace-level aqueous solution using a tannic acid (TA) coated single-wall carbon nanotube (SWCNT) film. The TA has many redox ligands that efficiently adsorb Au(III) from the solution and reduce them to Au particles. The interwoven SWCNTs not only act as a framework to improve the mechanical stability of the hybrid membrane, but also provide abundant paths for H2O transport, and facilitate the full exposure of the TA. As a result, the hybrid membrane has an excellent ability to capture gold ions from solution with a high flux of 157 L/(m2·h·bar), and an ultra-high adsorption capacity of 2095 mg/g from solutions with an extremely low gold concentration of 20 ppm. The adsorbed gold ions are reduced to Au particles, which can be easily collected by oxidation. The recovered Au nanoparticles on the TA–SWCNT hybrid film had a remarkable surface-enhanced Raman scattering effect that enabled the sensitive detection of rhodamine 6G.

Research Article Issue
High-throughput screening and machine learning for the efficient growth of high-quality single-wall carbon nanotubes
Nano Research 2021, 14 (12): 4610-4615
Published: 18 March 2021
Downloads:32

It has been a great challenge to optimize the growth conditions toward structure-controlled growth of single-wall carbon nanotubes (SWCNTs). Here, a high-throughput method combined with machine learning is reported that efficiently screens the growth conditions for the synthesis of high-quality SWCNTs. Patterned cobalt (Co) nanoparticles were deposited on a numerically marked silicon wafer as catalysts, and parameters of temperature, reduction time and carbon precursor were optimized. The crystallinity of the SWCNTs was characterized by Raman spectroscopy where the featured G/D peak intensity (IG/ID) was extracted automatically and mapped to the growth parameters to build a database. 1, 280 data were collected to train machine learning models. Random forest regression (RFR) showed high precision in predicting the growth conditions for high-quality SWCNTs, as validated by further chemical vapor deposition (CVD) growth. This method shows great potential in structure-controlled growth of SWCNTs.

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