Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

Abstract: System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Abstract: Aspects of the present disclosure relate to systems and methods for manufacturing biologically-produced pharmaceutical products. Some of the systems described herein comprise an upstream component comprising a bioreactor and at least one filter (e.g., a filter probe) integrated with a downstream component comprising a purification module comprising at least a first partitioning unit and a second partitioning unit. In some embodiments; these integrated biomanufacturing systems may be operated under continuous or conditions and may be capable of efficiently producing pure, high-quality pharmaceutical products.

Abstract: System, methods, and other embodiments described herein relate to improving the cycling of batteries by using data and a hierarchical Bayesian model (HBM) for predicting the cycle life of a cycling protocol. In one embodiment, a method includes classifying cycle life of a battery into a class using battery data from cycling with a protocol, wherein the class represents cycle life distributions of cycling protocols. The method also includes quantifying, using the class in a HBM, variability for the battery induced by the protocol. The method also includes predicting, using the HBM, an adjusted cycle life for the protocol according to the variability. The method also includes communicating the adjusted cycle life to operate the battery.

Abstract: System, methods, and other embodiments described herein relate to improving the estimation of battery life. In one embodiment, a method includes measuring electrochemical data of a battery cell associated with an electrochemical reaction triggered by a test during a diagnostic cycle. The method also includes determining a feature associated with the degradation of the battery cell from the electrochemical data. The method also includes predicting an end-of-life (EOL) of the battery cell by using the feature in a machine learning (ML) model.

Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.

Abstract: Aspects of the present disclosure relate to filtration systems and methods for production of biologically-produced products, which may include pharmaceutical and/or protein products. Certain biomanufacturing systems described herein comprise a bioreactor (e.g., a perfusion bioreactor, a chemostat) and a filter probe comprising a filter bundle comprising a plurality of hollow fibers (e.g., longitudinally aligned hollow fibers). According to some embodiments, a center-to-center distance between any two hollow fibers within the fiber bundle at one or more points along a length of the fiber bundle is relatively large (e.g., greater than or equal to an average outer diameter of the hollow fibers of the fiber bundle, greater than or equal to 1.1 times a minimum diameter of the two hollow fibers). In some embodiments, the hollow fibers within the fiber bundle are arranged in an array (e.g., a hexagonal, linear, annular, or square array).

Abstract: Systems and methods for generating and evaluating candidate sequences of partitioning steps to partition at least one biologically produced product from at least one impurity. In some embodiments, a plurality of candidate sequences of partitioning steps may be generated, wherein at least one candidate sequence of the plurality of candidate sequences comprises a plurality of partitioning steps in a specified order. The plurality of candidate sequences may be evaluated. For instance, a data set associated with the at least one partitioning step may be accessed, the data set comprising: first data indicative of a behavior of the at least one biologically produced product with respect to the at least one partitioning step; and second data indicative of a behavior of the at least one impurity with respect to the at least one partitioning step. The at least one candidate sequence may be scored based at least in part on the data set.

Abstract: Aspects of the present disclosure relate to systems and methods for manufacturing biologically-produced pharmaceutical products. Some of the systems described herein comprise an upstream component comprising a bioreactor and at least one filter (e.g., a filter probe) integrated with a downstream component comprising a purification module comprising at least a first partitioning unit and a second partitioning unit. In some embodiments, these integrated biomanufacturing systems may be operated under continuous or conditions and may be capable of efficiently producing pure, high-quality pharmaceutical products.

Abstract: A method of using data-driven predictive modeling to predict and classify battery cells by lifetime is provided that includes collecting a training dataset by cycling battery cells between a voltage V1 and a voltage V2, continuously measuring battery cell voltage, current, can temperature, and internal resistance during cycling, generating a discharge voltage curve for each cell that is dependent on a discharge capacity for a given cycle, calculating, using data from the discharge voltage curve, a cycle-to-cycle evolution of cell charge to output a cell voltage versus charge curve Q(V), generating transformations of ?Q(V), generating transformations of data streams that include capacity, temperature and internal resistance, applying a machine learning model to determine a combination of a subset of the transformations to predict cell operation characteristics, and applying the machine learning model to output the predicted battery operation characteristics.

Abstract: Aspects of the present disclosure relate to filtration systems and methods for production of biologically-produced products, which may include pharmaceutical and/or protein products. Certain biomanufacturing systems described herein comprise a bioreactor (e.g., a perfusion bioreactor, a chemostat) and a filter probe comprising a filter bundle comprising a plurality of hollow fibers (e.g., longitudinally aligned hollow fibers). According to some embodiments, a center-to-center distance between any two hollow fibers within the fiber bundle at one or more points along a length of the fiber bundle is relatively large (e.g., greater than or equal to an average outer diameter of the hollow fibers of the fiber bundle, greater than or equal to 1.1 times a minimum diameter of the two hollow fibers). In some embodiments, the hollow fibers within the fiber bundle are arranged in an array (e.g., a hexagonal, linear, annular, or square array).

Abstract: The disclosure describes an injection molding process for coating a tablet core to produce a coated pharmaceutical tablet, wherein the injection-molded coating is substantially continuous (e.g., completely covers the tablet core with no openings), and describes the resulting coated pharmaceutical tablet. The disclosure describes compositions for coatings and tablet cores and equipment suitable for performing the process.

Abstract: The present invention generally relates to devices and methods for crystallizing a compound. In certain industries, crystallization techniques require additional filtration steps in order to obtain products of relatively high yield and/or high purity. In some embodiments, the devices and methods described herein facilitate continuous production of high yield and/or high purity products without the need for additional filtration steps. In some embodiments, the devices and methods comprise flowing a fluid comprising a compound (e.g., a crystallizable compound, a solidifiable compound) over a substrate such that the compound crystallizes and/or precipitates on the substrate. In some embodiments, the crystallized compound can be recovered (e.g., at a high purity in solution). In certain embodiments, the substrate is orientated substantially vertically (e.g., such that flow of the fluid is driven by gravity). In some cases, the substrate comprises a plurality of crystallization promoting structures.