Abstract: Computer implemented method and systems for using machine learning to adjust a respective credit limit of one or more retailers that utilize a B2B marketplace platform to make product orders, are disclosed herein. Such a method includes collecting retailer data for retailers that utilized the B2B marketplace platform, and identifying cohorts based on the retailer data. The method also includes producing, based on the cohorts, a plurality of sets of ML models that include a set of risk ML models, a set of non-defaulter value ML models, and a set of defaulter value ML models, each of which sets include respective cohort-based ML scoring models and a cohort-based ML aggregation model. The method also includes using scores produces by the sets of ML models to determine whether, and to what extent, to adjust the credit limit of a retailer.

Abstract: A system, computer program product and a method to predict an objective function based on a recipe, and generate, via a machine learning model, a plurality of proposed different recipes of battery materials for optimizing at least objective function. Instances of the different proposed recipes of battery materials are prepared and deposited into an electrochemical module by a robotic preparation module. A robotic testing module executes a plurality of formulation characteristic tests on each deposited recipe instance and updates the machine learning model with a result of at least one of the formulation characteristic tests.

Abstract: A system, computer program product and a method to predict an objective function based on a recipe, and generate, via a machine learning model, a plurality of proposed different recipes of battery materials for optimizing at least objective function. Instances of the different proposed recipes of battery materials are prepared and deposited into an electrochemical module by a robotic preparation module. A robotic testing module executes a plurality of formulation characteristic tests on each deposited recipe instance and updates the machine learning model with a result of at least one of the formulation characteristic tests.

Abstract: Disclosed is a manufacturing method of 1,2-dichlorohexafluorocyclopentene. The first reaction uses dicyclopentadiene as a starting material and nitrogen gas or another inert gas as a diluting agent in a gas-phase thermal cracking reaction to obtain cyclopentadiene. The second reaction uses cyclopentadiene as a starting material in a liquid phase chlorination reaction with chlorine gas to obtain 1,2,3,4-tetrachlorocyclopentane. The third reaction uses 1,2,3,4-tetrachlorocyclopentane as a starting material in a gas-phase chlorination and fluorination reaction with hydrogen fluoride and chlorine gas in the presence of a chromium-based catalyst to obtain 1,2-dichlorohexafluorocyclopentene. The method uses easily acquired starting material and a stable fluorination catalyst, provides a high yield for a target product, and is applicable for large-scale continuous gas-phase production of 1,2-dichlorohexafluorocyclopentene.

Abstract: Disclosed is a manufacturing method of 1,2-dichlorohexafluorocyclopentene. The first reaction uses dicyclopentadiene as a starting material and nitrogen gas or another inert gas as a diluting agent in a gas-phase thermal cracking reaction to obtain cyclopentadiene. The second reaction uses cyclopentadiene as a starting material in a liquid phase chlorination reaction with chlorine gas to obtain 1,2,3,4-tetrachlorocyclopentane. The third reaction uses 1,2,3,4-tetrachlorocyclopentane as a starting material in a gas-phase chlorination and fluorination reaction with hydrogen fluoride and chlorine gas in the presence of a chromium-based catalyst to obtain 1,2-dichlorohexafluorocyclopentene. The method uses easily acquired starting material and a stable fluorination catalyst, provides a high yield for a target product, and is applicable for large-scale continuous gas-phase production of 1,2-dichlorohexafluorocyclopentene.