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The development of noble-metal-free heterogeneous catalysts that can realize the aerobic oxidation of C–H bonds at low temperature is a profound challenge in the catalysis community. Here we report the synthesis of a mesoporous Mn 0.5 Ce 0.5 O x solid solution that is highly active for the selective oxidation of hydrocarbons under mild conditions (100–120 °C). Notably, the catalytic performance achieved in the oxidation of cyclohexane to cyclohexanone/cyclohexanol (100 °C, conversion: 17.7%) is superior to those by the state-of-art commercial catalysts (140–160 °C, conversion: 3-5%). The high activity can be attributed to the formation of a Mn 0.5 Ce 0.5 O x solid solution with an ultrahigh manganese doping concentration in the CeO 2 cubic fluorite lattice, leading to maximum active surface oxygens for the activation of C–H bonds and highly reducible Mn 4+ ions for the rapid migration of oxygen vacancies from the bulk to the surface. Precious metal free catalysts for aerobic oxidation of hydrocarbons are industrially useful materials. Here, the authors report a mesoporous maganese-cerium oxide solid solution that is highly active for the selective oxidation of C-H bonds in hydrocarbons under mild conditions.

Single-atom catalysts have recently been applied in many applications such as CO oxidation. Experimental in situ investigations into this reaction, however, are limited. Hereby, we present a suite of operando/in situ spectroscopic experiments for structurally well-defined atomically dispersed Rh on phosphotungstic acid during CO oxidation. The identification of several key intermediates and the steady-state catalyst structure indicate that the reactions follow an unconventional Mars-van Krevelen mechanism and that the activation of O 2 is rate-limiting. In situ XPS confirms the contribution of the heteropoly acid support while in situ DRIFT spectroscopy consolidates the oxidation state and CO adsorption of Rh. As such, direct observation of three key components, i.e., metal center, support and substrate, is achieved, providing a clearer picture on CO oxidation on atomically dispersed Rh sites. The obtained information are used to engineer structurally similar catalysts that exhibit T 20 values up to 130 °C below the previously reported Rh 1 /NPTA. Single-atom catalysts have been studied for CO oxidation, but experimental in situ investigations are limited. Here, the authors use a suite of in situ/operando spectroscopy to identify key intermediates and define design principles to enhance the CO oxidation activity of atomically dispersed Rh on heteropoly acids.

Taming interfacial electronic effects on Pt nanoparticles modulated by their concomitants has emerged as an intriguing approach to optimize Pt catalytic performance. Here, we report Pt nanoparticles assembled on vacancy-abundant hexagonal boron nitride nanosheets and their use as a model catalyst to embrace an interfacial electronic effect on Pt induced by the nanosheets with N-vacancies and B-vacancies for superior CO oxidation catalysis. Experimental results indicate that strong interaction exists between Pt and the vacancies. Bader charge analysis shows that with Pt on B-vacancies, the nanosheets serve as a Lewis acid to accept electrons from Pt, and on the contrary, when Pt sits on N-vacancies, the nanosheets act as a Lewis base for donating electrons to Pt. The overall-electronic effect demonstrates an electron-rich feature of Pt after assembling on hexagonal boron nitride nanosheets. Such an interfacial electronic effect makes Pt favour the adsorption of O 2 , alleviating CO poisoning and promoting the catalysis. Tuning electronic properties of metallic catalysts is a useful way to improve their activity, however control over metal-support interactions is still challenging. Here the authors report a vacancy-induced interfacial electronic effect for Pt assembled on vacancy-abundant h -BN nanosheets leading to superior CO oxidation catalysis.

The vulnerability of modern neural networks to random noise and deliberate attacks has raised concerns about their robustness, particularly as they are increasingly utilized in safety- and security-critical applications. Although recent research efforts were made to enhance robustness through retraining with adversarial examples or employing data augmentation techniques, a comprehensive investigation into the effects of training data perturbations on model robustness remains lacking. This paper presents the first extensive empirical study investigating the influence of data perturbations during model retraining. The experimental analysis focuses on both random and adversarial robustness, following established practices in the field of robustness analysis. Various types of perturbations in different aspects of the dataset are explored, including input, label, and sampling distribution. Single-factor and multi-factor experiments are conducted to assess individual perturbations and their combinations. The findings provide insights into constructing high-quality training datasets for optimizing robustness and recommend the appropriate degree of training set perturbations that balance robustness and correctness, and contribute to understanding model robustness in deep learning and offer practical guidance for enhancing model performance through perturbed retraining, promoting the development of more reliable and trustworthy deep learning systems for safety-critical applications.

Ni-based catalysts are highly reactive for dry reforming of methane (DRM) but they are prone to rapid deactivation due to sintering and/or coking. In this study, we present a straightforward approach for anchoring dispersed Ni sites with strengthened metal-support interactions, which leads to Ni active sites embedded in dealuminated Beta zeolite with superior stability and rates for DRM. The process involves solid-state grinding of dealuminated Beta zeolites and nickel nitrate, followed by calcination under finely controlled gas flow conditions. By combining in situ X-ray absorption spectroscopy and ab initio simulations, it is elucidated that the efficient removal of byproducts during catalyst synthesis is conducted to strengthen Ni–Si interactions that suppress coking and sintering after 100 h of time-on-stream. Transient isotopic kinetic experiments shed light on the differences in intrinsic turnover frequency of Ni species and explain performance trends. This work constructs a fundamental understanding regarding the implication of facile synthesis protocols on metal-support interaction in zeolite-supported Ni sites, and it lays the needed foundations on how these interactions can be tuned for outstanding DRM performance. Ni-based catalysts are highly reactive for DRM, but they tend to deactivate quickly due to sintering and/or coking. Here a simple method for anchoring dispersed Ni sites in dealuminated Beta zeolite, enhancing metal-support interactions, results in a catalyst with superior stability and performance for DRM.

Engineering strong metal–support interactions (SMSI) is an effective strategy for tuning structures and performances of supported metal catalysts but induces poor exposure of active sites. Here, we demonstrate a strong metal–support interaction via a reverse route (SMSIR) by starting from the final morphology of SMSI (fully-encapsulated core–shell structure) to obtain the intermediate state with desirable exposure of metal sites. Using core–shell nanoparticles (NPs) as a building block, the Pd–FeO x NPs are transformed into a porous yolk–shell structure along with the formation of SMSIR upon treatment under a reductive atmosphere. The final structure, denoted as Pd–Fe 3 O 4 –H, exhibits excellent catalytic performance in semi-hydrogenation of acetylene with 100% conversion and 85.1% selectivity to ethylene at 80 °C. Detailed electron microscopic and spectroscopic experiments coupled with computational modeling demonstrate that the compelling performance stems from the SMSIR, favoring the formation of surface hydrogen on Pd instead of hydride. Strong metal–support interactions (SMSI) are effective in tuning the structures and catalytic performances of catalysts but limited by the poor exposure of active sites. Here, the authors develop a strategy to engineer SMSI via a reverse route, which is in favor of metal site exposure while embracing the SMSI.

It is crucial to develop a catalyst made of earth-abundant elements highly active for a complete oxidation of methane at a relatively low temperature. NiCo 2 O 4 consisting of earth-abundant elements which can completely oxidize methane in the temperature range of 350–550 °C. Being a cost-effective catalyst, NiCo 2 O 4 exhibits activity higher than precious-metal-based catalysts. Here we report that the higher catalytic activity at the relatively low temperature results from the integration of nickel cations, cobalt cations and surface lattice oxygen atoms/oxygen vacancies at the atomic scale. In situ studies of complete oxidation of methane on NiCo 2 O 4 and theoretical simulations show that methane dissociates to methyl on nickel cations and then couple with surface lattice oxygen atoms to form –CH 3 O with a following dehydrogenation to −CH 2 O; a following oxidative dehydrogenation forms CHO; CHO is transformed to product molecules through two different sub-pathways including dehydrogenation of OCHO and CO oxidation. The development of methane oxidation catalysts made of earth-abundant elements is an important challenge. Here, the authors report a cost-effective nickel-cobalt oxide which outperforms precious-metal-based alternatives, due to the combination of transition metal cations and surface oxygen vacancies.

Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance the stability, activity, and selectivity of heterogeneous catalysts. While charge transfer is tunable using the atomic structure and chemistry of the catalyst-support interface, direct experimental evidence is missing for three-dimensional catalyst nanoparticles, primarily due to the lack of a high-resolution method that can probe and correlate both the charge distribution and atomic structure of catalyst/support interfaces in these structures. We demonstrate a robust scanning transmission electron microscopy (STEM) method that simultaneously visualizes the atomic-scale structure and sub-nanometer-scale charge distribution in heterogeneous catalysts using a model Au-catalyst/SrTiO 3 -support system. Using this method, we further reveal the atomic-scale mechanisms responsible for the highly active perimeter sites and demonstrate that the charge transfer behavior can be readily controlled using post-synthesis treatments. This methodology provides a blueprint for better understanding the role of charge transfer in catalyst stability and performance and facilitates the future development of highly active advanced catalysts. Precise control of charge transfer between catalyst nanoparticles and supports presents a unique opportunity to enhance catalytic performance. Here the authors demonstrate a scanning transmission electron microscopy method to visualize atomic-scale structure and sub-nanometer-scale charge distribution in heterogeneous catalysts, shedding light on the atomic-scale mechanisms behind their highly active perimeter sites.

Styrene is an important commodity chemical that is highly energy and CO 2 intensive to produce. We report a redox oxidative dehydrogenation (redox-ODH) strategy to efficiently produce styrene. Facilitated by a multifunctional (Ca/Mn) 1− x O@KFeO 2 core-shell redox catalyst which acts as (i) a heterogeneous catalyst, (ii) an oxygen separation agent, and (iii) a selective hydrogen combustion material, redox-ODH auto-thermally converts ethylbenzene to styrene with up to 97% single-pass conversion and >94% selectivity. This represents a 72% yield increase compared to commercial dehydrogenation on a relative basis, leading to 82% energy savings and 79% CO 2 emission reduction. The redox catalyst is composed of a catalytically active KFeO 2 shell and a (Ca/Mn) 1− x O core for reversible lattice oxygen storage and donation. The lattice oxygen donation from (Ca/Mn) 1− x O sacrificially stabilizes Fe 3+ in the shell to maintain high catalytic activity and coke resistance. From a practical standpoint, the redox catalyst exhibits excellent long-term performance under industrially compatible conditions. Styrene is an important commodity chemical that is highly energy and CO 2 intensive to produce. Here, authors report a redox-oxidative dehydrogenation scheme and a tailored core-shell redox catalyst to convert ethylbenzene to styrene with up to 91.4% single-pass yield and 82% energy savings.

Spiking neural network (SNN) is a brain-inspired model with more spatio-temporal information processing capacity and computational energy efficiency. However, with the increasing depth of SNNs, the memory problem caused by the weights of SNNs has gradually attracted attention. In this study, we propose an ultra-low latency adaptive local binary spiking neural network (ALBSNN) with accuracy loss estimators, which dynamically selects the network layers to be binarized to ensure a balance between quantization degree and classification accuracy by evaluating the error caused by the binarized weights during the network learning process. At the same time, to accelerate the training speed of the network, the global average pooling (GAP) layer is introduced to replace the fully connected layers by combining convolution and pooling. Finally, to further reduce the error caused by the binary weight, we propose binary weight optimization (BWO), which updates the overall weight by directly adjusting the binary weight. This method further reduces the loss of the network that reaches the training bottleneck. The combination of the above methods balances the network's quantization and recognition ability, enabling the network to maintain the recognition capability equivalent to the full precision network and reduce the storage space by more than 20%. So, SNNs can use a small number of time steps to obtain better recognition accuracy. In the extreme case of using only a one-time step, we still can achieve 93.39, 92.12, and 69.55% testing accuracy on three traditional static datasets, Fashion- MNIST, CIFAR-10, and CIFAR-100, respectively. At the same time, we evaluate our method on neuromorphic N-MNIST, CIFAR10-DVS, and IBM DVS128 Gesture datasets and achieve advanced accuracy in SNN with binary weights. Our network has greater advantages in terms of storage resources and training time.