Predicting a Key Catalyst-Performance Descriptor for Supported Metal Nanoparticles: Metal Chemical Potential

Efficient Photothermal Sensor Based on Coral-Like Hollow Gold Nanospheres for the Sensitive Detection of Sulfonamides

Gold nanoparticles (AuNPs), universally regarded as colorimetric signal reporters, are widely employed in lateral flow immunoassays (LFIAs). However, it is difficult for AuNPs-LFIA to achieve a wide range and sensitive detection. Herein, novel coral-like hollow gold nanospheres (CHGNPs) are synthesized. The growth of gold nanospheres can be regulated to obtain a multibranched and hollow construction. The obtained CHGNPs possess intense broadband absorption across the visible to near-infrared region, exhibiting a high molar extinction coefficient of 14.65 × 1011 M-1 cm-1 and a photothermal conversion efficiency of 79.75%. Thus, the photothermal/colorimetric dual-readout LFIA is developed based on CHGNPs (CHGNPs-PT-LFIA and CHGNPs-CM-LFIA) to effectively improve the detection sensitivity and broaden the detection range in regard to sulfonamides (SAs). The limits of detection of the CHGNPs-PT-LFIA and CHGNPs-CM-LFIA reached 1.9 and 2.8 pg mL-1 for the quantitative detection of sulfaquinoxaline, respectively, which are 6.3-fold and 4.3-fold lower than that of the AuNPs-LFIA. Meanwhile, the CHGNPs-PT-LFIA broadened the detection range to three orders of magnitude, which ranged from 2.5 to 5000 pg mL-1. The synthesized photothermal CHGNPs have been proven effective in improving the performance of the LFIA and provide a potential option for the construction of sensing platforms.

MonteCat: A Basin-Hopping-Inspired Catalyst Descriptor Search Algorithm for Machine Learning Models

Proposing relevant catalyst descriptors that can relate the information on a catalyst's composition to its actual performance is an ongoing area in catalyst informatics, as it is a necessary step to improve our understanding on the target reactions. Herein, a small descriptor-engineered data set containing 3289 descriptor variables and the performance of 200 catalysts for the oxidative coupling of methane (OCM) is analyzed, and a descriptor search algorithm based on the workflow of the Basin-hopping optimization methodology is proposed to select the descriptors that better fit a predictive model. The algorithm, which can be considered wrapper in nature, consists of the successive generation of random-based modifications to the descriptor subset used in a regression model and adopting them depending on their effect on the model's score. The results are presented after being tested on linear and Support Vector Regression models with average cross-validation r2 scores of 0.8268 and 0.6875, respectively.

Toward a new definition of surface energy for late transition metals

Surface energy is a top-importance stability descriptor of transition metal-based catalysts. Here, we combined density functional theory (DFT) calculations and a tiling scheme measuring surface areas of metal structures to develop a simple computational model predicting the average surface energy of metal structures independently of their shape. The metals considered are W, Ru, Co, Ir, Ni, Pd, Pt, Cu, Ag and Au. Lorentzian trends derived from the DFT data proved effective at predicting the surface energy of metallic surfaces but not for metal clusters. We used machine-learning protocols to build an algorithm that improves the Lorentzian trend's accuracy and is able to predict the surface energies of metal surfaces of any crystal structure, i.e., face-centred cubic, hexagonal close-packed, and body-centred cubic, but also of nanostructures and sub-nanometer clusters. The machine-learning neural network takes easy-to-compute geometric features to predict metallic moieties surface energies with a mean absolute error of 0.091 J m^-2 and an R² score of 0.97.

Direct Dry Synthesis of Supported Bimetallic Catalysts: A Study on Comminution and Alloying of Metal Nanoparticles

Ball milling is growing increasingly important as an alternative synthetic tool to prepare catalytic materials. It was recently observed that supported metal catalysts could be directly obtained upon ball milling from the coarse powders of metal and oxide support. Moreover, when two compatible metal sources are simultaneously subjected to the mechanochemical treatment, bimetallic nanoparticles are obtained. A systematic investigation was extended to different metals and supports to understand better the mechanisms involved in the comminution and alloying of metal nanoparticles. Based on this, a model describing the role of metal-support interactions in the synthesis was developed. The findings will be helpful for the future rational design of supported metal catalysts via dry ball milling.

CO2 Activation over Nanoshaped CeO2 Decorated with Nickel for Low-Temperature Methane Dry Reforming

Dry reforming of methane (DRM) is a promising way to convert methane and carbon dioxide into H₂ and CO (syngas). CeO₂ nanorods, nanocubes, and nanospheres were decorated with 1-4 wt % Ni. The materials were structurally characterized using TEM and in situ XANES/EXAFS. The CO₂ activation was analyzed by DFT and temperature-programmed techniques combined with MS-DRIFTS. Synthesized CeO₂ morphologies expose {111} and {100} terminating facets, varying the strength of the CO₂ interaction and redox properties, which influence the CO₂ activation. Temperature-programmed CO₂ DRIFTS analysis revealed that under hydrogen-lean conditions mono- and bidentate carbonates are hydrogenated to formate intermediates, which decompose to H₂O and CO. In excess hydrogen, methane is the preferred reaction product. The CeO₂ cubes favor the formation of a polydentate carbonate species, which is an inert spectator during DRM at 500 °C. Polydentate covers a considerable fraction of ceria's surface, resulting in less-abundant surface sites for CO₂ dissociation.

Combining artificial intelligence and physics-based modeling to directly assess atomic site stabilities: from sub-nanometer clusters to extended surfaces

The performance of functional materials is dictated by chemical and structural properties of individual atomic sites. In catalysts, for example, the thermodynamic stability of constituting atomic sites is a key descriptor from which more complex properties, such as molecular adsorption energies and reaction rates, can be derived. In this study, we present a widely applicable machine learning (ML) approach to instantaneously compute the stability of individual atomic sites in structurally and electronically complex nano-materials. Conventionally, we determine such site stabilities using computationally intensive first-principles calculations. With our approach, we predict the stability of atomic sites in sub-nanometer metal clusters of 3-55 atoms with mean absolute errors in the range of 0.11-0.14 eV. To extract physical insights from the ML model, we introduce a genetic algorithm (GA) for feature selection. This algorithm distills the key structural and chemical properties governing the stability of atomic sites in size-selected nanoparticles, allowing for physical interpretability of the models and revealing structure-property relationships. The results of the GA are generally model and materials specific. In the limit of large nanoparticles, the GA identifies features consistent with physics-based models for metal-metal interactions. By combining the ML model with the physics-based model, we predict atomic site stabilities in real time for structures ranging from sub-nanometer metal clusters (3-55 atom) to larger nanoparticles (147 to 309 atoms) to extended surfaces using a physically interpretable framework. Finally, we present a proof of principle showcasing how our approach can determine stable and active nanocatalysts across a generic materials space of structure and composition.