Nanoparticles of nickel phosphide are finding wide ranging utility as catalysts for hydrodesulfurization, hydrogen evolution reaction, and hydrodeoxygenation of bio-oils. Herein, we present a methodology to tailor monodisperse nickel phosphide nanoparticles in terms of size and phase through the use of a statistical response surface methodology. Colloidal nickel phosphide nanoparticles were synthesized by replacing octadecene (ODE), a commonly used organic solvent, by a more sustainable phosphonium-based ionic liquid (IL). The replacement of ODE with the phosphonium-based IL resulted in faster crystallization at lower temperatures to yield phase-pure, monodisperse Ni2P nanoparticles. Using a first-order design, the PPh3/Ni precursor ratio was identified as the most critical factor influencing the resulting size and phase of the nanoparticles. Optimization using a Doehlert matrix for second-order design yielded a second-degree polynomial equation used to predict the mean diameter of the nanoparticles (over a range of 4–12 nm) as a function of the PPh3/Ni precursor ratio and the temperature used during synthesis. The resulting model was validated by performing reactions using randomly chosen sets of conditions; the experimentally determined nanoparticle sizes were in excellent agreement with the theoretical sizes predicted by our model. This demonstrates the utility of a multivariate experimental design as a powerful tool in the development of synthetic strategies toward the preparation of colloidal nanoparticles with highly controlled size, size distribution, and phase.

The understanding and control of colloidal nanocrystal syntheses are essential for discovery and optimization of desired properties and therefore play a key role in the applications of these materials. Typical one variable at a time (OVAT) methods limit the ability of researchers to achieve such goals by providing one-dimensional insight into a complex, multidimensional experimental domain, wasting precious resources in the process. Design of experiments (DoE) in conjunction with response surface methodology (RSM) offers an accelerated route for multivariate investigation and optimization of nanocrystal syntheses. The method enables systematic analysis and multidimensional modeling of the independent and dependent effects that any number of factors have on chosen responses, resulting in easy optimization of a large synthetic space in a fraction of the experiments. Herein, we will outline the general steps to follow when utilizing DoE and RSM for screening and optimization of nanocrystal syntheses, as well as the background needed to appropriately design an investigation and understand the results.

Thiospinels, such as CoNi2S4, are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNi2S4 has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNi2S4 nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 25–2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/d̅) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.

In this work, the synthesis of silver and copper nanoparticles and bimetallic silver-platinum and copper-platinum nanoparticles, in previously functionalized multi-walled carbon nanotubes (MWCNT), was carried out using the intermatrix synthesis for the charge of the first metal, and a galvanic replacement for the deposition of a second metal to form the bimetallic NPs. Well-controlled small size NPs were obtained as demonstrated by TEM, with a homogeneous distribution and mean particle diameters of ca. 2.9 nm. The hybrid MNPs/MWCNTs catalysts were characterized by FTIR, TEM and XPS. The metal content was determined by TGA and validated via FAAS. Thereupon, the metal-MWCNTs hybrid catalysts were incorporated into a polymeric membrane (PM) and characterized by SEM. The effects of the hybrid catalyst-polymeric support interactions and the role of the MNPs/MWCNTs/PM materials as heterogeneous catalysts were evaluated from the catalytic performance on the reduction of 4-nitrophenol as a model reaction. An apparent rate constant normalized by the metal content of 1706.7 s −1 mol −1 was achieved for the best system (Ag-PtNPs/MWCNTs/PMR) along with a decrease in the percentage of conversion from 95% (first cycle) to 80% (third cycle). Results indicated that the catalytic activity depends mainly on the MNPs size and the metal content in the catalyst. The catalytic activity of the MNPs/MWCNTs was only 3 times higher than for the MNPs/MWCNTs/PMs catalysts, with the former presenting the advantage of being easily recovered from the reaction medium, thus, demonstrating the capability to perform an efficient and sustainable process.

A polymeric membrane-supported catalyst with immobilized gold nanoparticles (AuNPs) was prepared through the extraction and in situ reduction of Au salts in a one-step strategy. Polymeric inclusion membranes (PIMs) and polymeric nanoporous membranes (PNMs) were tested as different membrane-support systems. Transport experiments indicated that PIMs composed of cellulose triacetate, 2-nitrophenyloctyl ether, and an aliphatic tertiary amine (Adogen 364 or Alamine 336) were the most efficient supports for Au extraction. The simultaneous extraction and reduction processes were proven to be the result of a synergic phenomenon in which all the membrane components were involved. Scanning electron microscopy characterization of cross-sectional samples suggested a distribution of AuNPs throughout the membrane. Transmission electron microscopy characterization of the AuNPs indicated average particle sizes of 36.7 and 2.9 nm for the PIMs and PNMs, respectively. AuNPs supported on PIMs allowed for >95.4 % reduction of a 0.05 mmol L 4-nitrophenol aqueous solution with 10 mmol L NaBH solution within 25 min.