颗粒分配器和颗粒分配器:通过自动化材料复合探索纳米复合材料空间的开放式硬件

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-08-30 DOI:10.1039/D4DD00198B
Miguel Hernández-del-Valle, Jorge Ilarraza-Zuazo, Enrique Dios-Lázaro, Javier Rubio, Joris Audoux and Maciej Haranczyk
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摘要

新型聚合物基纳米复合材料的开发需要对大量成分进行实验制备和表征,以确定最佳配方。对于热塑性材料,混料过程通常涉及聚合物和添加剂等固体成分的分配、称重、混合和挤出等劳动密集型任务。在此,我们介绍一种开放式硬件解决方案,旨在实现这一过程的自动化。我们的设置系统旨在简化与实验设计或闭环自动驾驶实验室相关的材料测量任务。我们的硬件设置由两个主要部分组成:一个是多材料颗粒分配器,可简化从一系列母料中制备目标成分的过程;另一个是颗粒收集器-分配器,可在整个实验过程中有效地将处理过的材料收集并分配到各种容器中。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Pellet dispensomixer and pellet distributor: open hardware for nanocomposite space exploration via automated material compounding†

The development of novel polymer-based nanocomposites necessitates the experimental preparation and characterization of numerous compositions to identify optimal formulations. For thermoplastic-based materials, the compounding process typically involves the labor-intensive tasks of dispensing, weighing, mixing, and extruding solid components such as polymers and additives. Herein, we present an open hardware solution that aims to automate this process. Our setup system is designed to streamline material surveying tasks associated with experimental design or closed-loop, self-driving laboratories. Our hardware setup consists of two main components: a multi-material pellet dispenser, which simplifies the preparation of targeted compositions from a range of master batches, and a pellet collector-distributor, which efficiently gathers and distributes processed materials into various containers throughout the experiment.

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Back cover ArcaNN: automated enhanced sampling generation of training sets for chemically reactive machine learning interatomic potentials. Sorting polyolefins with near-infrared spectroscopy: identification of optimal data analysis pipelines and machine learning classifiers†‡ High accuracy uncertainty-aware interatomic force modeling with equivariant Bayesian neural networks† Correction: A smile is all you need: predicting limiting activity coefficients from SMILES with natural language processing
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