Methods and systems suitable for tracking Brownian motion of particles suspended in a fluid and determining the diffusion coefficient of the particles therefrom in order to characterize the particles, their synthesis, and/or their surface modifications. The methods include providing a sample having particles suspended in a fluid, obtaining and recording at least first and second images of the sample wherein the first image obtained at a first time and the second image subsequently obtained at a second time, determining the average displacement of the particles in an area of the first and second images during a time period between the first time and the second time based on the first and second images, and then determining a diffusion coefficient of the particles in the area of the first and second images based on the average displacement of the particles during the time period.

Holograms of colloidal dispersions encode comprehensive information about individual particles' three-dimensional positions, sizes and optical properties. Extracting that information typically is computation-ally intensive, and thus slow. Machine-learning techniques based on support vector machines (SVMs) can analyze holographic video microscopy data in real time on low-power computers. The resulting stream of precise particle-resolved tracking and characterization data provides unparalleled insights into the composition and dynamics of colloidal dispersions and enables applications ranging from basic research to process control and quality assurance.

A gaseous-fluid environmental sensor having a gaseous-fluid flow system. The gaseous-fluid flow system, in one construction, includes a blower to move the gaseous fluid from an intake port to an exhaust port via a flow path. The gaseous-fluid environmental sensor further includes a controller coupled to a particle count sensor. The controller determines whether the particle count sensor has a fault based on the particle count sensor not sensing a particle or a cosmic ray in a time period.

A device for detecting the presence of pollen in the air, including a measuring chamber isolated from external light, an arrangement configured to drive an air flow through the measuring chamber, and a light source emitting a light beam in a direction of propagation through the air flow, into the measuring chamber. The device includes at least four photosensitive sensors configured to measure the luminous flux diffused by the illuminated air flow, in four different directions, a clock, at least two meteorological sensors, and at least one computer capable of determining the nature of a pollen particle present in the air from the data measured by the photosensitive sensors, the clock and the meteorological sensors.

A system for determining a vehicle operating state is provided. The system includes at least two particle detectors, a controller and a memory. A sample volume used by each particle detector of the at least two particle detectors configured to be collected in a different location relative to the vehicle than another sample volume used by another particle detector of the at least two particle detectors and at least one sample volume is configured to be collected in an environment where particles are disturbed by the vehicle. The controller is configured to determine at least one operating state of the vehicle based at least in part on a comparison of output signals of the at least two particle detectors. The at least one memory is used to store at least operating instructions implemented by the controller in determining the at least one operating state of the vehicle.

A method and device for optically detecting particles, including: (a) a cell wall of rectangular cross-section is fitted on a longitudinal surface and adjoining transverse surface with an L-shaped heating and cooling element; (b) the center of the transverse surface of the cell wall opposite the transverse surface which forms the support of the cell wall is irradiated by an irradiation device and is observed at right angles to the optical axis of the irradiation device; (c) the focus of the irradiation device and the observation device can be moved by a motor to any point in the three-dimensional inner region defined by the cell wall; (d) the surface of the cell wall opposite the optical glass window through which the radiation from the irradiation device enters comprises another optical glass window; (e) the temperature of the surface of the cell wall is monitored by two thermistors.

Dispersed nanoparticles in a liquid dispersion sample, using an electronic data processor for classifying the sample as having, or not having, nanoparticles present. An electronic data processor is used for pre-training a machine learning classifier, including by emitting a laser modulated by a modulation frequency onto each specimen; capturing a temporal signal from laser light, calculating specimen DCT or Wavelet transform coefficients from the captured signal, using the calculated coefficients to pre-train the machine learning classifier, using a laser emitter having a focusing optical system to emit a laser onto the sample, and using a light receiver to capture a signal from laser light backscattered A sample DCT or Wavelet transform coefficients are calculated and the pre-trained machine learning classifier used to classify the calculated sample coefficients as having, or not having, nanoparticles present.

Methods for detecting and analyzing individual nanoparticles of the same, similar, or different sizes co-existing in a fluid sample using multi-spectral analysis are disclosed. A plurality of light sources may be configured to produce a plurality of light beams at different spectral wavebands. An optical assembly may be configured to combine the plurality of light beams into one or more incident light sheets. Each incident light sheet may illuminate one or more nanoparticles in a liquid sample. One or more image detectors may be configured to detect, using a plurality of wavelengths, light scattered or emitted by one or more nanoparticles. The plurality of wavelengths may correspond to the different spectral wavebands of the plurality of light beams. Related apparatus, systems, techniques, and articles are also described.

A method and device for optically detecting particles (23) have the following features: (a) a cell wall (9) of rectangular cross-section, made of black glass, is fitted on a longitudinal surface and adjoining transverse surface with an L-shaped heating and cooling element (1); (b) the centre of the transverse surface of the cell wall (9) opposite the transverse surface which forms the support of the cell wall (9) is irradiated by an irradiation device and is observed at right angles to the optical axis of the irradiation device by means of an observation device; (c) the focus of the irradiation device and the focus of the observation device can be moved by a motor to any point in the three-dimensional inner region defined by the cell wall (9) by means of a control device; (d) the surface of the cell wall (9) opposite the optical glass window (11) through which the radiation from the irradiation device enters comprises another optical glass window (11) in the centre thereof; (e) the temperature of the surface of the cell wall (9) is monitored by means of two thermistors (8).

A method is described for determining the size of a transparent particle (2), wherein the particle (2) is illuminated with light from a light source (6), wherein using a radiation detector (7) a time-resolved intensity curve of light from the light source (6) scattered on the particle (2) is measured at a preselectable scattering angle .sub.s, wherein characteristic scattered light peaks are determined in the intensity curve, and wherein the size of the particle (2) is determined on the basis of the time difference between two scattered light peaks, characterized in that, with the help of two radiation detectors (7) or light sources (6), a first and a second time-resolved intensity curve of scattered light, scattered on the particle (2) in the forward direction, are measured; a transmission peak (12) and a reflection peak (11) are determined from the first intensity curve and from the second intensity curve; a first time difference between the transmission peaks (12) is determined, and a second time difference between the reflection peaks (11) is determined; a characteristic variable is determined from the ratio of the first time difference and the second time difference; and a size determination is performed for the particles (2) for which the characteristic variable corresponds to a preselectable value. (FIG. 3)