用于揭示周期表上电催化剂性能真正控制因素的高通量操作光谱技术博士奖学金项目——致力于开发自动化原位光谱技术，以理解实现碳中和转型过程中电催化剂性能的周期性变化规律

该研究项目旨在开发 HiSPEC-2 高通量原位光谱电化学平台，以测量电催化剂的性能。其目标是通过应用机器学习技术分析物理性能特征，并构建综合数据集，从而推动可再生能源领域催化剂的研发。申请者应拥有化学或材料科学等相关领域的学位。

Job description: Details: Supervisor: Dr ［会员专属信息］ Application deadline: December 20 th 2026 Interview date: January 15 th 2027 UCL Chemistry Department is offering a fully funded studentship to a highly motivated candidate to start in February 2027. The student will carry out their doctoral research at UCL. Machine learning has transformed drug discovery, yet a comparable acceleration has not happened for clean-energy materials such as electrocatalysts for carbon-neutral fuels. Billions of dollars are being bet on vast compute and datasets outcompeting physical understanding. This gamble may backfire: the nanoscale interfaces that control catalyst performance often emerge or evolve only during operation, so the parameters that describe them can only be measured operando. Operando spectroscopy, however, is slow, painstaking and incompatible with high-throughput approaches. High throughput operando spectroscopy modules that any lab could use would greatly improve the prospects of AI-driven materials discovery. It would also let us answer a fundamental question for the first time: do the parameters long thought to dominate catalyst performance really control activity in real materials, or are their effects confounded and drowned out by other parameters when many materials are compared at scale? We have recently developed HiSPEC a high-throughput in-situ UV-Vis spectroelectrochemistry platform that measures the binding energies of electrocatalysts. In this project you will develop the second iteration of this instrument HiSPEC-2; automating in-situ UV-Vis and IR spectroelectrochemistry systems, and apply machine learning to extract the physical performance descriptors (e.g. substrate binding energies) that govern how electro- and photocatalysts behave across the periodic table. Measuring these descriptors systematically across many materials will show which of them genuinely predict performance and which are masked by competing effects, while producing the first large, consistent operando dataset to accelerate the discovery of catalysts for renewable hydrogen and carbon-neutral chemicals. You will gain highly sought-after, transferable expertise in automation, spectroscopy, electro-/photochemistry and data science within the ASEM group ( asem-lab.org ). You will be able to shape your own project, with additional supervision from experts in X-ray characterisation and AI, and the potential to travel to the USA, China and Japan.

资助说明： 请注意，只有英国公民、持有定居或预定居身份的欧盟学生以及拥有无限期居留许可的学生有资格申请该奖学金，因为该奖学金仅涵盖学费。关于明年学费资格的更新规定，可访问网站查看。