Automated synthesis and data accumulation for fast production of high-performance Ni nanocatalysts

Bimetal–Organic Frameworks for High Efficiency Catalytic Reduction

“…As seen in Figure g, it shows good linear correlations of ln( c t / c 0 ) versus t for the reduction reaction. The k app (slope of the linearly fitted plots) is 0.153 s –1 ; the catalytic activity of the CuNi-MOFs (Figure f,g) is 34 times higher than that of Ni-MOFs (0.0045 s –1 , Figure d,e) as well as some novel metals in previous reports − (Table S2). It is worth noting that a higher k app indicates higher catalytic activity in the catalytic reduction of 4-NP by NaBH 4 .…”

“…On the basis of results and recent reports, ,,,− the reduction process with the presence of CuNi-MOF catalysts and NaBH 4 includes (Figure a) (1) chemisorption of BH 4 – on the surface of the MOFs and (2) transfer electrons from BH 4 – to the reactants via the Cu and Ni sites states. The metal site states of MOF can effectively trap and accumulate long-lived electrons, and act as relay for the electrons transfer from BH 4 – to 4-NP, which is a crucial process in real catalytic systems.…”

A new automated synthesis of a coke-resistant Cs-promoted Ni-supported nanocatalyst for sustainable dry reforming of methane

“…Next, the reactor containing the powder mixture was placed in the sample holder of the automated AIO reaction apparatus. 31 Then, the automated sequence proceeded at the set values of six steps: aging, cooling, heating, calcination, cooling, and purging (Fig. 1, Table S1, and Movie S1 in the ESI†).…”

“…In recent years, automated devices and high-throughput experiments have been developed for the synthesis and evaluation of nanomaterials. 31–35 In addition, computational simulation using density functional theory (DFT) of surface reactions has become an effective tool for the design and performance elucidation of new catalysts by using energies calculated from the activation and reaction of active nanomaterial surfaces. 36–39 Here, we report a new method for the optimized synthesis of a uniform Cs-promoted Ni/Al 2 O 3 nanocatalyst (U-(Cs)Ni/Al 2 O 3 ) with highly loaded tiny Ni nanoparticles ( ca.…”

Active Machine Learning for Chemical Engineers: a Bright Future Lies Ahead!

“…The use of automated units is of course not self-evident as these often are expensive and currently not well-suited for every problem. For example, despite past efforts [60] the automated synthesis and testing of catalysts is a challenging task, definitely when studying a broad design space [61]. By coupling these systems with active machine learning techniques, a huge time saving is expected for experimental campaigns, which will speed up reaction and catalyst optimization, and the acquisition of scientific knowledge.…”