High throughput methodology for investigating green hydrogen generating processes using colorimetric detection films and machine vision†

IF 6.2 Q1 CHEMISTRY, MULTIDISCIPLINARY Digital discovery Pub Date : 2024-06-13 DOI:10.1039/D4DD00070F
Savannah Talledo, Andrew Kubaney, Mitchell A. Baumer, Keegan Pietrak and Stefan Bernhard
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Abstract

The generation of hydrogen from abundant and renewable precursors driven by sunlight will be a cornerstone of a future, sustainable hydrogen infrastructure. Current methods to monitor the evolution of hydrogen in such photocatalytic systems such as gas chromatography, mass spectrometry, manometry or Raman spectroscopy are either expensive and low throughput or lack sensitivity and selectivity over other gasses. These impediments hinder the generation of photo-driven hydrogen evolution data necessary for machine learning and artificial intelligence-based protocols. This work presents an open-source approach for studying solar-driven hydrogen evolution reactions (HERs) in parallel that uses colorimetric hydrogen detection films in tandem with an image analysis software capable of providing metrics such as hydrogen amount, hydrogen evolution rates, incubation times, and plateau times. The sensing medium is composed of 0.05% (w/w) Pt impregnated molybdenum(VI) oxide or tungsten(VI) oxide which was incorporated into poly(vinyl alcohol) films placed under clear, gas impermeable septa. To conduct experiments, users require only blue reaction-driving high intensity LEDs (light emitting diodes), a camera, and uniform lighting to take pictures as the septa darken. This work introduces a sample configuration in which nine samples in hydrogen sensitive septa-capped vials were illuminated and the gas evolution is monitored using a RaspberryPi for image capture and storage. Two calibration methods are presented, one uses a gravimetric hydrogen evolution with Zn/HCl that is compared to a direct hydrogen injection. Both methods allow the accurate correlation of normalized intensity values of film photographs to mole fractions of H2 ranging from 0 to 50%. Four light-driven HERs are described that highlight the capabilities of the detection method, two of which were conducted using the novel septa-based instrumentation while the other two experiments used the films on a 108 multiwell plate using a previously described photoreactor.

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利用比色检测膜和机器视觉的高通量方法研究绿色制氢工艺
在阳光的驱动下,利用丰富的可再生前体生成氢气将成为未来可持续氢基础设施的基石。目前监测此类光催化系统中氢气进化的方法,如气相色谱法、质谱法、测压法或拉曼光谱法,要么价格昂贵、通量低,要么缺乏对其他气体的灵敏度和选择性。这些障碍阻碍了机器学习和人工智能协议所需的光驱动氢演化数据的生成。本研究提出了一种并行研究太阳能驱动的氢进化反应(HERs)的开源方法,该方法使用比色氢检测膜和图像分析软件,能够提供氢量、氢进化速率、孵育时间和高原时间等指标。传感介质由 0.05%(重量/湿重)铂浸渍氧化钼或氧化钨组成,铂浸渍氧化钼或氧化钨被掺入聚乙烯醇薄膜中,置于透明、气体不渗透的隔膜下。要进行实验,用户只需要蓝色反应驱动的高强度 LED(发光二极管)、照相机和均匀的照明,即可在隔膜变暗时拍照。这项工作介绍了一种样品配置,使用 RaspberryPi 采集和存储图像,对氢敏感隔膜盖小瓶中的九种样品进行照明,并监测气体演化。该研究介绍了两种校准方法,一种是使用 Zn/HCl 重力氢气进化法,另一种是直接氢气注入法。这两种方法都能将胶片照片的归一化强度值与 0 至 50% 的 H2 摩尔分数准确关联起来。报告介绍了四种光驱动氢氧还原法,突出说明了该检测方法的能力,其中两项实验是使用基于隔膜的新型仪器进行的,而另外两项实验则是在 108 多孔板上使用薄膜,并使用之前描述的光反应器进行的。
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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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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