The present invention provides a method for analysis and determination of the heavy metal occurrence key mineral phases in industrial solid waste, by performing N concentration gradients dissociation determination of the heavy metal solid waste to be tested under the same dissociation conditions, to give the dissociation degrees of the heavy metal elements to be tested at N different concentration gradients; the dissociated solid residues after dissociation being quantitatively analyzed for the mineral phase, to give the relative content of each mineral phase in the M mineral phases of the heavy metal solid waste to be tested; then calculating to give the occurrence distribution proportion of the heavy metal elements in the mineral phase, which are accumulated from high to low; the occurrence key mineral phase whose cumulative occurrence proportion exceeds the preset cumulative threshold value is determined to be the key mineral phase of the heavy metal elements.

The present invention provides a method for analysis and determination of the heavy metal occurrence key mineral phases in industrial solid waste, by performing N concentration gradients dissociation determination of the heavy metal solid waste to be tested under the same dissociation conditions, to give the dissociation degrees of the heavy metal elements to be tested at N different concentration gradients; the dissociated solid residues after dissociation being quantitatively analyzed for the mineral phase, to give the relative content of each mineral phase in the M mineral phases of the heavy metal solid waste to be tested; then calculating to give the occurrence distribution proportion of the heavy metal elements in the mineral phase, which are accumulated from high to low; the occurrence key mineral phase whose cumulative occurrence proportion exceeds the preset cumulative threshold value is determined to be the key mineral phase of the heavy metal elements.

A method for determination of molecular parameters for a configuration of a known single organic molecule embedded in an anisotropic environment generated by alignment media, said known single organic molecule comprising particles, is disclosed. The method comprising the steps of: a) Defining a three-dimensional grid that is aligned with the known atomic structure of the alignment medium; b) Placing the particles of the known single organic molecule on the respective grid points of the three-dimensional grid in relation to at least one assigned atom of the alignment medium; c) Determining the interaction between the particles of the single organic molecule and the alignment medium for a set of orientations and a plurality of configurations of the particles; d) Calculating anisotropic parameters obtainable by measuring with nuclear magnetic resonance (NMR) spectroscopy by use of the determined interactions for each of the plurality of configurations of the organic molecule.

A method for determination of molecular parameters for a configuration of a known single organic molecule embedded in an anisotropic environment generated by alignment media, said known single organic molecule comprising particles, is disclosed. The method comprising the steps of: a) Defining a three-dimensional grid that is aligned with the known atomic structure of the alignment medium; b) Placing the particles of the known single organic molecule on the respective grid points of the three-dimensional grid in relation to at least one assigned atom of the alignment medium; c) Determining the interaction between the particles of the single organic molecule and the alignment medium for a set of orientations and a plurality of configurations of the particles; d) Calculating anisotropic parameters obtainable by measuring with nuclear magnetic resonance (NMR) spectroscopy by use of the determined interactions for each of the plurality of configurations of the organic molecule.

Devices, systems and methods for sorting and labeling food products are provided. Respective spectra of food products for a plurality of segments of a line are received at a controller from at least one line-scan dispersive spectrometer configured to acquire respective spectra of the food products for the plurality of segments of the line. The controller applies one or more machine learning algorithms to the respective spectra to classify the plurality of segments according to at least one of one or more food parameters. The controller controls one or more of a sorting device and a labeling device according to classifying the plurality of segments to cause the food products to be one or more of sorted and labeled according to the at least one of the one or more food parameters.

Microscopy methods for determining embryo viability are described. A method can include accessing, at a compute device, fluorescence lifetime imaging microscopy (FLIM) data set associated with a biological material. The biological material can include either an embryo or a gamete. The method further includes extracting a fluorescence photon arrival time from a subset of data from the FLIM data set. The method further includes estimating a likelihood that the biological material will produce a successful pregnancy and/or a live birth based on the fluorescence photon arrival time histogram and an estimation model that has been trained using artificial intelligence and labeled clinical training data. The method includes generating an output signal representing the estimated likelihood that the biological material will produce a successful pregnancy and/or a live birth. In some embodiments, the method can include training the estimation model using a plurality of fluorescence photon arrival time histograms of the FLIM data set.

Microscopy methods for determining embryo viability are described. A method can include accessing, at a compute device, fluorescence lifetime imaging microscopy (FLIM) data set associated with a biological material. The biological material can include either an embryo or a gamete. The method further includes extracting a fluorescence photon arrival time from a subset of data from the FLIM data set. The method further includes estimating a likelihood that the biological material will produce a successful pregnancy and/or a live birth based on the fluorescence photon arrival time histogram and an estimation model that has been trained using artificial intelligence and labeled clinical training data. The method includes generating an output signal representing the estimated likelihood that the biological material will produce a successful pregnancy and/or a live birth. In some embodiments, the method can include training the estimation model using a plurality of fluorescence photon arrival time histograms of the FLIM data set.

Methods, systems, and devices associated with a smart scent library are described. An apparatus can include a processing resource and a memory resource having instructions executable to store data representing a number of chemical compositions in a smart scent library. The instructions can be executable to receive data, including a chemical composition, and compare the chemical composition to the number of chemical compositions. The instructions can be executable to identify the chemical composition in response to matching at least a portion of the chemical composition to one of the number of chemical compositions, and transmit data associated with the identified chemical composition.

The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.

The present disclosure relates to techniques for deformulating the spectra of arbitrary compound formulations such as polymer formulations into their chemical components. Particularly, aspects of the present disclosure are directed to obtaining an initial set of spectra for a plurality of samples comprising pure samples and composite samples, constructing a basis set of spectra for a plurality of pure samples based on the initial set of spectra, and providing or outputting the basis set of spectrum. The basis set of spectra is constructed in an iterative process that attempts to decompose, using a decomposition algorithm or model, the spectrum from the initial set of spectra in order to differentiate the pure samples from the composite samples. The basis set of spectra may then be used to deduce the composition of a material from a spectrogram.