A method and a device for determining the material properties of a polycrystalline, in particular metallic, product during production or quality control of the polycrystalline, in particular metallic, product by means of X-ray diffraction using at least one X-ray source and at least one X-ray detector. In this case, an X-ray generated by the X-ray source is directed onto a surface of the polycrystalline product and the resulting diffraction image of the X-ray is recorded by the X-ray detector. After exiting the X-ray source, the X-ray is passed through an X-ray mirror, wherein the X-ray is both monochromatized and focused, by the X-ray mirror, in the direction of the polycrystalline product and/or the X-ray detector, and then reaches a surface of the metallic product.

A material analysis method is provided. A plurality of wafers processed from a plurality of ingots are measured by a measuring instrument to obtain an average of a bow of each of the wafers processed from the ingots and a plurality of full widths at half maximum (FWHM) of each of the wafers. Key factors respectively corresponding to the ingots are calculated according to the FWHM of the wafers. A regression equation is obtained according to the key factors and the average of the bows.

This invention patent application addresses a system for the detection and quantification of mineralogical species via x-ray diffraction (XRD) of the concentrate of dry copper before it is injected into a converter or melting furnace. Specifically, it addresses a device that performs a mineralogical analysis, in line and in real time, of the concentrate of copper in the bath smelting furnace via x-ray diffraction (XRD), which allows for control over the ideal mixture for the optimal process for copper sulfide (Cu2S)-white metal, iron sulfide (FeS)-Slag and pyritic sulfur (S2)-temperature.

Disclosed is a sulfate corrosion-resistant concrete, a method for optimizing proportion and application thereof. The concrete is formed by mixing and stirring base stocks, aggregates, admixtures, external additives and water. The base stock of the concrete is 17.4-17.5 parts of Portland cement; the aggregates include 38.9 parts of basalt with aggregate size of 5-10 mm and 33.1-33.2 parts of basalt medium sand; the admixtures are 1.9-1.95 parts of silica fume or fly ash, and further including 0.23-0.24 part of polycarboxylate water reducer and 1.34-1.35 part of sulfate corrosion-resistant liquid preservative. Optimized proportion method: according to the corrosion characteristics of sulfate and corrosion environment parameters, determine the composition and proportion of basic samples and comparison samples, make and cure sample components, test the deep components of the samples, and obtain the optimal composition and proportion according to the test results.

The present disclosure introduces methods for characterizing iron core carbohydrate colloid drug products, such as iron sucrose drug products. Disclosed methods enable the characterization of the iron core size of the iron core nanoparticles in iron carbohydrates as they exist in the formulation in solution, such as e.g. iron sucrose drug products, and more particularly, the average particle diameter size and size distribution(s) of the iron core nanoparticles. The disclosed methods apply small-angle X-ray scattering (SAXS) in parallel beam transmission geometry, with a sample mounted inside a capillary and centered in the X-ray beam, to iron carbohydrates, such as iron sucrose, in solution without the need to modify the sample, such as to remove unbound carbohydrates, dilute, or dry the sample, to accurately characterize the average iron core particle diameter size of the iron core nanoparticles. An example application of the disclosed method is to perform SAXS measurements under identical instrument settings on two samples of the same type of iron core nanoparticle colloid drug product for the purpose of comparing their iron core structures. Such comparisons are typically performed during the iron core carbohydrate colloid drug development process, and can include comparisons of samples that have been manipulated.

A system of collating a spectral data of an arbitrary material with spectral data of existing materials to identify the kind of the arbitrary material comprises a one-dimensional CNN processor calculating a characteristic value vector based on a spectral data of a material by a one-dimensional convolution neural network algorithm, and a metric learning processor computing a probability that the kind of the material is each kind of the existing materials from the characteristic value vector by a deep metric learning algorithm. The processors learn with the spectral data of existing materials to compute a probability for the kind of each material such that the probabilities for the kinds of the respective materials inputted for data for learning becomes maximum. When the data of the arbitrary material is inputted, the kind giving the maximum probability is identified as the kind of the arbitrary material with high precision.

An x-ray microscopy method that obtains a classification of different particles by distinguishing between different material phases through a combination of image processing involving morphological edge enhancement and possibly resolved absorption contrast differences between the phases along with optional wavelet filtering.

Disclosed are fibers which contain identification fibers. The identification fibers can comprise one or more chemical markers, or taggants, which may vary among the fibers or be incorporated throughout all of the fibers. The disclosure also relates to the method for making and characterizing the fibers. Characterization of the fibers can include identifying chemical markers and correlating the chemical markers and a taggant chemical marker amounts of at least one of the chemical markers to manufacturer-specific taggants to determine supply chain information. The supply chain information can be used to track the fibers from manufacturing through intermediaries, conversion to final product, and/or the consumer.

The present disclosure concerns an electron microscopy method, including the emission of a precessing electron beam and the acquisition, at least partly simultaneous, of an electron diffraction pattern and of intensity values of X rays.

According to an aspect of the present invention, provided is an information processing apparatus comprising a memory configured to store a program; and a processor configured to execute a program so as to output a parameter result in relation to a thin film by inputting a profile result in relation to an intensity of X-ray from the thin film to a neural network, wherein the neural network is a neural network that is allowed to machine-learn teacher data using profile data in relation to an intensity of X-ray from a thin film as input data and using parameter data in relation to the thin film as output data.