Method For Detection Of Nanoparticles Research Articles

Smart optical measurement probe for autonomously detecting nano-defects on bare semiconductor wafer surface: highly sensitive observation system using phase-contrast microscopy with a spatial light modulator

Nowadays, the yield rate of semiconductor process is strongly affected even by nanoscale defects on bare Si wafer surfaces. Therefore, a non-destructive and high-speed method for detection of nanoparticle contamination is highly demanded. In this research, a nanoparticle detection method is proposed based on the fact that the volatilization process of the volatile inert liquid behaves as an autonomous nanoparticle search probe. Dynamic phase contrast microscopy (Dynamic PCM) is proposed to detect small thickness changes of a thin liquid film with high sensitivity. The dynamic PCM is expected to have double sensitivity compared to normal PCM and it is experimentally verified. Based on previous results, 4% intensity change is expected for 10 nm particles and even over 1.5% intensity change is expected for 5 nm particles by using this proposed dynamic PCM.

Mode splitting revealed by Fano interference

An optical whispering gallery mode (WGM) resonator supports degenerate counter-propagating modes and the degeneracy is lifted as mode splitting due to Rayleigh scattering. However, quantitative analysis becomes difficult if the resonance experiences weak scattering. Here we develop a spectroscopical method to identify an arbitrary small scatterer using the Fano interference-induced spectral response modification. Scattering information can be revealed by fitting the responses as a function of the field’s phase and intensity. In addition, we show that this modified response helps achieve an ultra-low detection limit for the mode-splitting-based nanoparticle detection method. This approach may be promising in the characterization of high-Q-factor devices, novel sensing methods, and quantum coupling system investigation.

Automatic detection, localization and segmentation of nano-particles with deep learning in microscopy images

With the growing amount of high resolution microscopy images automatic nano-particle detection, shape analysis and size determination have gained importance for providing quantitative support that gives important information for the evaluation of the material. In this paper, we present a new method for detection of nano-particles and determination of their shapes and sizes simultaneously with deep learning. The proposed method employs multiple output convolutional neural networks (MO-CNN) and has two outputs: first is the detection output that gives the locations of the particles and the other one is the segmentation output for providing the boundaries of the nano-particles. The final sizes of particles are determined with the modified Hough algorithm that runs on the segmentation output. The proposed method is tested and evaluated on a dataset containing 17 TEM images of Fe3O4 and silica coated nano-particles. Also, we compared these results with U-net algorithm which is a popular deep learning method. The experiments showed that the proposed method has 98.23% accuracy for detection and 96.59% accuracy for segmentation of nano-particles.

Rapid detection of ultra-trace nanoparticles based on ACEK enrichment for semiconductor manufacturing quality control

Commercial fabrication of semiconductor circuits is currently conducted with nanometer resolution. As a result, trace amount of nanoparticles in semiconductor processing fluids have a great influence on the yield of semiconductor chips, which was only a minor problem in the past when semiconductors were processed at larger critical dimensions. Therefore, a highly sensitive and rapid method for the online detection of nanoparticles is highly sought after by semiconductor industry. The work presents a highly sensitive, easy to do, and rapid method for detection of nanoparticles in liquid, which is based on AC electrokinetics enhanced capacitive-sensing method using commercial microelectrode chips. The detection limit reaches as low as 0.1 part per trillion with a response time of 60 s. Testing conditions of AC voltage and frequency were also investigated to obtain the optimized test parameters. Compared with the other existing detection methods, this method is highly sensitive, of low cost and with a fast response, which is important to realize high-efficiency and -reliability quality control of semiconductor process.

In-house validation of a method for determination of silver nanoparticles in chicken meat based on asymmetric flow field-flow fractionation and inductively coupled plasma mass spectrometric detection

Nanomaterials are increasingly used in food production and packaging, and validated methods for detection of nanoparticles (NPs) in foodstuffs need to be developed both for regulatory purposes and product development. Asymmetric flow field-flow fractionation with inductively coupled plasma mass spectrometric detection (AF4-ICP-MS) was applied for quantitative analysis of silver nanoparticles (AgNPs) in a chicken meat matrix following enzymatic sample preparation. For the first time an analytical validation of nanoparticle detection in a food matrix by AF4-ICP-MS has been carried out and the results showed repeatable and intermediately reproducible determination of AgNP mass fraction and size. The findings demonstrated the potential of AF4-ICP-MS for quantitative analysis of NPs in complex food matrices for use in food monitoring and control. The accurate determination of AgNP size distribution remained challenging due to the lack of certified size standards.

Influence of Hydrazine-Induced Aggregation on the Electrochemical Detection of Platinum Nanoparticles

To study the catalytic activity of single nanoparticles (NPs) electrochemically, we investigated the applicability of a novel method for nanoparticle detection as a means to immobilize individual NPs. This method consists of analyzing the current steps that can be measured at an ultramicroelectrode (UME) when a colloid of NPs is injected into an electrolyte containing an electroactive species, that is turned over at the NP but not the UME surface. We have measured these current steps for the hydrazine oxidation at Pt NPs landing on a lithographically fabricated Au UME, showing a mean step size comparable to theory and prior measurements. We found a reduced landing frequency with respect to values reported in the literature and those predicted from theory, while the current step distribution showed a long tail of large current steps. This could be explained by the particle aggregation, which would lower the effective NP concentration and therefore lower the landing frequency and would result in higher current steps when aggregates reach the electrode. Cyclic voltammetry (CV) measurements of the Pt-modified Au UME showed a signal characteristic of the presence of Pt, while electron microscopy revealed aggregated NPs, after landings were performed in the presence of hydrazine or hydrogen gas. Conversely, no aggregates were found after particles were injected in absence of such reducing agents, while CV still suggested the presence of Pt, indicating individual particles. The finding, that landing nanoparticles in the presence of hydrazine yields NP aggregates on the surface, means that this particular method is currently not suited for the preparation of individually immobilized particles to facilitate catalysis studies at individual nanoparticles.

Novel nanoparticle detection method using electrochemical device based on anodic aluminum oxide nanopore membrane

Some investigations suggest that nanoparticles are potentially the most dangerous because they can also penetrate deeper into lung tissue than other large particles. However, some researchers declare that the research on security of environmental nanoparticles should be on the basis of the standardization of investigation system. Therefore, it is significant to detect nanoparticles for both environmental evaluation and further treatment. In this paper, a method for iron oxide nanoparticle detection was investigated by a novel electrochemical device based on AAO nanopore membrane and preliminary results was taken out which may create novel avenues and applications for nanoparticle detection.

Detection of aquatic colloids in drinking water during its distribution via a water pipeline network

Laser-induced Breakdown Detection (LIBD) is a highly sensitive method for the direct detection of nano-particles (colloids). During the detection process plasmas are generated on single particles by a focused laser beam, the resulting plasma light emissions are detected optically. The method is based on the difference in breakdown thresholds of liquid and solid matter, it is lower for solid material. The laser pulse energy is adjusted precisely so that in the pure liquid no breakdown events occur, and only in the presence of colloids is the breakdown threshold in the focal volume exceeded. The spatial distribution of several thousand recorded plasma flashes within the focal volume reveals the mean particle diameter. The evaluation of the number of breakdown events per number of laser pulses results in a breakdown probability, together with the particle size the concentration is calculated using specially-designed computer software. Compared to conventional laser light scattering methods the LIBD is approximately 6 orders of magnitude more sensitive for particles smaller than ca. 0.05 pm. Together with laser light obscuration the LIBD technique has been used successfully for the quantification of aquatic nano-particles during drinking water processing and its distribution via a pipeline network of nearly 1,700 km total length. In addition, the particulate content of several brands of mineral water has been investigated.

Development of Photon-Counting Laser-Light-Scattering Method for Detection of Nano-Particles Formed in CVD Plasmas.

Development of Photon-Counting Laser-Light-Scattering Method for Detection of Nano-Particles Formed in CVD Plasmas.