A particle detection device includes a detection tube, a light emitter, a light receiver, and a processing unit. The detection tube is for a detection solution to pass through. The light emitter generates a detection light and emits the detection light to the detection solution. The light receiver receives the detection light scattered from the detection solution. The processing unit generates a received light intensity value according to the detection signal generated by the light receiver, and determines whether the received light intensity value is greater than a first threshold value: if greater, generating a detection result of particles; otherwise, generating a detection result of no particles. Then it provides a basis for semiconductor manufacturing companies to evaluate whether the detection solution can be used in a high-precision manufacturing processes, thereby optimizing the manufacturing process and improving the yield rate of the high-precision manufacturing process.

The present disclosure relates to a field programmable gate array (FPGA)-based multi-channel dynamic light scattering (DLS) autocorrelation system and method. The system includes a DLS generation apparatus, a photon correlator, and a host computer, where the photon correlator includes an FPGA and a universal serial bus (USB) communication module; the DLS generation apparatus is connected to the FPGA; the FPGA is configured to count and perform correlation calculation on photon pulses generated by the DLS generation apparatus; the USB communication module is connected to the host computer; the FPGA includes a dual counter module and a correlation calculation module; the dual counter module is connected to the DLS generation apparatus and the correlation calculation module; the correlation calculation module is connected to the USB communication module; the dual counter module includes a plurality of dual counters; and the correlation calculation module includes a plurality of correlators.

A detection device for tiny particles in a liquid is provided. The detection device comprises includes a flow cell, a laser, a scattered light collection device, a photoelectric detector, a fiber Bragg grating and a first optical fiber coupler, wherein scattered light collected by the scattered light collection device is sent to the fiber Bragg grating through the first optical fiber coupler, and reflected light of the fiber Bragg grating after receiving the scattered light is sent to the photoelectric detector through the first optical fiber coupler. The device can eliminate most scattered light generated by the liquid, and reduce the interference of the scattered light of the liquid to scattered light signals generated by the particles, so that the scattered light signals captured by the photoelectric detector are mainly light signals generated by the particles.

An apparatus for sensing particulate matter in a fluid includes a fluid flow conduit fluidically connected to an interaction chamber; a light source positioned to illuminate the interaction chamber; and a light detector assembly positioned to receive light scattered by particulate matter present in the interaction chamber. The light detector assembly includes a light detector; and an optical element, the optical element configured to provide light to the light detector based on an incidence angle of the scattered light.

A spectral method is provided for partitioning type and loading with aerosol optical depth. Based on multi-spectral optical aerosol depth, particle-size distribution and refractive index are derived by normalizing first- and second-order derivatives for processing quantitative calibration of main components. According to the optical feature parameters of various aerosol types, a radiation theory is applied to simulate multi-spectral optical depth for each density, including those of mixed types. The intrinsic parameters of aerosol types are figured out by constructing normalized derivative aerosol indices (NDAI). The clear characteristic differences between aerosol types are used to figure out main components of aerosols and their mixing ratios. The simulation result of the normalized index of various aerosol type is in good agreement with the ground observation data of Aerosol Robotic Network. It shows that NDAI is quite practicable in quantitative calibration of main components of atmospheric aerosol.

A particle size distribution measuring apparatus includes a light source that emits measurement light to a sample accommodated in a cell including a pair of light transmission plates separated from each other, one or a plurality of detectors that detects the measurement light scattered in the sample, and a particle size distribution calculator that calculates a particle size distribution of a particle group included in the sample based on output signals of the detectors. The particle size distribution measuring apparatus further includes a force applying mechanism that moves at least one of the light transmission plates to apply pressure or a shearing force to the sample in the cell, in which the particle size distribution calculator is configured to calculate the particle size distribution at the time when the pressure or the shearing force applied to the sample has changed from a first state to a second state.

Disclosed herein is a system for performing urinalysis of transurethral patients. The system includes a tubing set to receive urine from a urethral catheter. A detector assembly is operatively coupled between the tubing set and a urinalysis module coupled. The system can perform urinalysis of a urine sample disposed within the tubing set and render urinalysis information on a display of the module. Also disclosed is a method of performing urinalysis that can include operations of: (i) placing a urine sample within a cuvette of a urinalysis system, the cuvette including a lumen extending between an inlet and an outlet; (ii) projecting coherent light into the sample; (iii) collecting output light exiting the sample; (iv)extracting urinalysis data from the collected light; and (v) rendering urinalysis results on a display of the system.

Method, device, and system for identifying a model-based time dependent light scattering signature that includes receiving an experimental time dependent light scattering signature comprising experimental data descriptive of an average molecular weight of protein components in a solution over time. The method further includes identifying an Ansatz for evaluating the experimental time dependent light scattering signature, the Ansatz being an initial model-based time dependent light scattering signature, the initial model-based time dependent light scattering signature identifying at least one key variable. The method also includes adjusting the at least one key variable in the initial model-based time dependent light scattering signature until a final model-based time dependent light scattering signature is identified. In some instances, the final model-based time dependent light scattering signature identifies at least one protein aggregation mechanism.

This fluid evaluation device is provided with an irradiation unit for irradiating a fluid with light, a light reception unit for receiving scattered light from the fluid and outputting a light reception signal, and an estimation unit for estimating at least one from among flow rate and density by mapping input points, which are on a first plane defined by flow rate and frequency and are expressed by light amount information indicating the amount of scattered light included in the light reception signal and frequency information indicating a frequency for a beat signal resulting from the Doppler shifting of the light included in the light reception signal, onto a second plane defined by fluid flow rate and fluid density.

The disclosure features systems and methods for measuring and diagnosing target constituents bound to labeling particles in a sample. The systems include a radiation source, a sample holder, a detector configured to obtain one or more diffraction patterns of the sample each including information corresponding to optical properties of sample constituents, and an electronic processor configured to, for each of the one or more diffraction patterns: (a) analyze the diffraction pattern to obtain amplitude information and phase information corresponding to the sample constituents; (b) identify one or more particle-bound target sample constituents based on at least one of the amplitude information and the phase information; and (c) determine an amount of at least one of the particle-bound target sample constituents in the sample based on at least one of the amplitude information and the phase information.