Abstract: Methods, systems, and apparatus, including computer programs encoded on computer-storage media, for microplastic removal. In some implementations, a method can include controlling a camera to capture one or more images of plastic in water; providing the one or more images to a machine learning model trained to detect plastic; obtaining output from the machine learning model indicating one or more items of plastic; and controlling one or more acoustic transducers to move the one or more items of plastic.

Abstract: Techniques for selecting a spectroscopic light source include obtaining a light source dataset and a spectroscopic dataset, initializing a genetic algorithm, selecting a first individual solution and a second individual solution from an initial generation of solutions, generating a new individual solution from the first and second individual solutions by combining their respective chromosome encodings, evaluating a specificity of the new individual solution to a target material, adding the new individual solution to a new generation of solutions, populating the new generation of solutions with a plurality of additional individual solutions, generating one or more descendent generations of solutions by iterating the genetic algorithm, selecting one or more implementation individual solutions exhibiting a threshold specificity to the target material, and outputting the one or more implementation individual solutions.

Abstract: Techniques for selecting a spectroscopic light source include obtaining a light source dataset and a spectroscopic dataset, initializing a genetic algorithm, selecting a first individual solution and a second individual solution from an initial generation of solutions, generating a new individual solution from the first and second individual solutions by combining their respective chromosome encodings, evaluating a specificity of the new individual solution to a target material, adding the new individual solution to a new generation of solutions, populating the new generation of solutions with a plurality of additional individual solutions, generating one or more descendent generations of solutions by iterating the genetic algorithm, selecting one or more implementation individual solutions exhibiting a threshold specificity to the target material, and outputting the one or more implementation individual solutions.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: Techniques for selecting a spectroscopic light source include obtaining a light source dataset and a spectroscopic dataset, initializing a genetic algorithm, selecting a first individual solution and a second individual solution from an initial generation of solutions, generating a new individual solution from the first and second individual solutions by combining their respective chromosome encodings, evaluating a specificity of the new individual solution to a target material, adding the new individual solution to a new generation of solutions, populating the new generation of solutions with a plurality of additional individual solutions, generating one or more descendent generations of solutions by iterating the genetic algorithm, selecting one or more implementation individual solutions exhibiting a threshold specificity to the target material, and outputting the one or more implementation individual solutions.

Abstract: Systems and methods for managing chemical recycling processes include accessing characterization data of a feedstock, the characterization data comprising one or more spectra collected according to one or more spectroscopic methods. The methods include predicting, using the characterization data, a set of constituent materials included in the feedstock. The methods include predicting a material composition of the feedstock using the predicted set of constituent materials. The methods include identifying, at least in part using the predicted material composition of the feedstock, one or more target products. The methods include generating a set of chemical reaction schemas enabling a conversion of at least part of the feedstock into the one or more target products. The methods also include storing identifications of the material composition of the feedstock, the one or more target products, and the set of chemical reaction schemas in a data store.

Abstract: A method for generating spectroscopic data includes inputting, by a computing device, a text string comprising a structural representation of a material to an encoder of a natural language processing (NLP) model implemented with a deep neural network. The method includes generating, using the encoder of the NLP model, an encoded representation of the text string. The text string may include latent chemical bond information of the material. The method includes mapping, by the computing device, the encoded representation including the latent chemical bond information to a spectrum array, the spectrum array including predicted spectroscopic data of the material. The method also includes outputting, by the computing device, the spectrum array.

Abstract: 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.