Abstract: CsPbI3 is a promising perovskite material for photovoltaic applications in its photoactive perovskite or black phase. However, the material degrades to a photovoltaically inactive or yellow phase at room temperature. Various mitigation strategies are currently being developed to increase the lifetime of the black phase, many of which rely on inducing strains in the material that hinder the black-to-yellow phase transition. Physical insight into how these strategies exactly induce strain as well as knowledge of the spatial extent over which these strains impact the material is crucial to optimize these approaches but is still lacking. Herein, we combine machine learning potential-based molecular dynamics simulations with our in silico strain engineering approach to accurately quantify strained large-scale atomic structures on a nanosecond time scale. To this end, we first model the strain fields introduced by atomic substitutions as they form the most elementary strain sources. We demonstrate that the magnitude of the induced strain fields decays exponentially with the distance from the strain source, following a decay rate that is largely independent of the specific substitution. Second, we show that the total strain field induced by multiple strain sources can be predicted to an excellent approximation by summing the strain fields of each individual source. Finally, through a case study, we illustrate how this additive character allows us to explain how complex strain fields, induced by spatially extended strain sources, can be predicted by adequately combining the strain fields caused by local strain sources. Hence, the strain additivity proposed here can be adopted to further our insight into the complex strain behavior in perovskites and to design strain from the atomic level onward to enhance their sought-after phase stability.

Abstract: Plasma catalysis has emerged as a promising approach for driving thermodynamically unfavorable chemical
reactions. Nevertheless, comprehending the mechanisms involved remains a challenge, leading to uncertainty
about whether the optimal catalyst in plasma catalysis aligns with that in thermal catalysis. In this research, we
explore this question by studying monometallic catalysts (Fe, Co, Ni and Mo) and bimetallic catalysts (Fe-Co, Mo-
Co, Fe-Ni and Mo-Ni) in both thermal catalytic and plasma catalytic NH3 decomposition. Our findings reveal that
the Fe-Co bimetallic catalyst exhibits the highest activity in thermal catalysis, the Fe-Ni bimetallic catalyst
outperforms others in plasma catalysis, indicating a discrepancy between the optimal catalysts for the two
catalytic modes in NH3 decomposition. Comprehensive catalyst characterization, kinetic analysis, temperature
program surface reaction experiments and plasma diagnosis are employed to discuss the key factors influencing
NH3 decomposition performance.

Abstract: technologies have been expanding, and one of the most exciting and rapidly growing
applications is in biology and medicine. Most biomedical studies with DBD plasma systems are performed in vitro, which include cells grown on the surface of plastic well plates, or in vivo, which include animal research models (e.g. mice, pigs). Since many DBD systems use the biological target as the secondary electrode for direct plasma generation and treatment, they are sensitive to the surface properties of the target, and thus can be altered based on the in vitro or in vivo system used. This could consequently affect biological response from plasma treatment. Therefore, in this study, we investigated the DBD plasma behavior both in vitro (i.e. 96-well flat bottom plates, 96-well U-bottom plates, and 24-well flat bottom plates), and in vivo (i.e. mouse skin). Intensified charge coupled device (ICCD) imaging was performed and the plasma discharges were visually distinguishable between the different systems. The geometry of the wells did not affect DBD plasma generation for low application distances (≤ 2 mm), but differentially affected plasma uniformity on the bottom of the well at greater distances. Since DBD plasma treatment in vitro is rarely performed in dry wells for plasma medicine experiments, the effect of well wetness was also investigated. In all in vitro cases, the uniformity of the DBD plasma was affected when comparing wet versus dry wells, with the plasma in the wide-bottom wells appearing the most similar to plasma generated on mouse skin. Interestingly, based on quantification of ICCD images, the DBD plasma intensity per surface area demonstrated an exponential one-phase decay with increasing application distance, regardless of the in vitro or in vivo system. This trend is similar to that of the energy per pulse of plasma, which is used to determine the total plasma treatment energy for biological systems. Optical emission spectroscopy performed on the plasma revealed similar trends in radical species generation between the plastic well plates and mouse skin. Therefore, taken together, DBD plasma intensity per surface area may be a valuable parameter to be used as a simple method for in situ monitoring during biological treatment and active plasma treatment control, which can be applied for in vitro and in vivo systems.

Abstract: Ruddlesden-Popper La????Sr2−????MnO4−???? materials are interesting symmetric solid oxide
fuel cell electrodes due to their good redox stability, mixed ionic and electronic conducting behavior and thermal expansion that matches well with common electrolytes. In reducing environments – as at a solid oxide fuel cell anode – the x = 0.5 member, i.e. La0.5Sr1.5MnO4−????, has a much higher total conductivity than compounds with a different La/Sr ratio, although all those compositions have the same K2NiF4-type I4/mmm structure. The origin for this conductivity difference is not yet known in literature. Now, a combination of in-situ and ex-situ 3D electron diffraction, high-resolution imaging, energy-dispersive X-ray analysis and electron energy-loss spectroscopy uncovered clear differences between x=0.25 and x=0.5 in the pristine structure, as well as in the transformations upon high-temperature reduction. In La0.5Sr1.5MnO4−????, Ruddlesden-Popper n=2 layer defects and an amorphous surface layer are present, but not in La0.25Sr1.75MnO4−????. After annealing at 700°C in 5% H2/Ar, La0.25Sr1.75MnO4−???? transforms to a tetragonal 2D incommensurately modulated structure with modulation vectors ⃗????1 = 0.2848(1) · (⃗????* +⃗????*) and ⃗????2 =0.2848(1) · (⃗????* – ⃗????*), whereas La0.5Sr1.5MnO4−???? only partially transforms to an orthorhombic 1D incommensurately modulated structure,
with ⃗???? = 0.318(2) · ⃗????*. Perovskite domains grow at the crystal edge at 700°C in 5%
H2 or vacuum, due to the higher La concentration on the surface compared to the bulk, which leads to a different thermodynamic equilibrium. Since it is known that a lower degree of oxygen vacancy ordering and a higher amount of perovskite blocks enhance oxygen mobility, those differences in defect structure and structural transformation upon reduction, might all contribute to the higher conductivity of La0.5Sr1.5MnO4−???? in solid oxide fuel cell anode conditions compared to other La/Sr ratios.

Abstract: Plasma catalysis is a rapidly growing field, often employing a packed-bed dielectric barrier discharge plasma reactor. Such dielectric barrier discharges are complex, especially when a packing material (e.g., a catalyst) is introduced in the discharge volume. Catalysts are known to affect the plasma discharge, though the underlying mechanisms influencing the plasma physics are not fully understood. Moreover, the effect of the catalysts on the plasma discharge and its subsequent effect on the overall performance is often overlooked. In this work, we deliberately design and synthesize catalysts to affect the plasma discharge in different ways. These Ni or Co alumina-based catalysts are used in plasma-catalytic dry reforming of methane and ammonia synthesis. Our work shows that introducing a metal to the dielectric packing can affect the plasma discharge, and that the distribution of the metal is crucial in this regard. Further, the altered discharge can greatly influence the overall performance. In an atmospheric pressure dielectric barrier discharge reactor, this apparently more uniform plasma yields a significantly better performance for ammonia synthesis compared to the more conventional filamentary discharge, while it underperforms in dry reforming of methane. This study stresses the importance of analyzing the plasma discharge in plasma catalysis experiments. We hope this work encourages a more critical view on the plasma discharge characteristics when studying various catalysts in a plasma reactor.

Abstract: This study investigates the dry reformation of methane (DRM) over Ni/Al2O3 catalysts in a dielectric barrier discharge (DBD) non-thermal plasma reactor. A novel hybrid machine learning (ML) model is developed to optimize the plasma-catalytic DRM reaction with limited experimental data. To address the non-linear and complex nature of the plasma-catalytic DRM process, the hybrid ML model integrates three well-established algorithms: regression trees, support vector regression, and artificial neural networks. A genetic algorithm (GA) is then used to optimize the hyperparameters of each algorithm within the hybrid ML model. The ML model achieved excellent agreement with the experimental data, demonstrating its efficacy in accurately predicting and optimizing the DRM process. The model was subsequently used to investigate the impact of various operating parameters on the plasma-catalytic DRM performance. We found that the optimal discharge power (20 W), CO2/CH4 molar ratio (1.5), and Ni loading (7.8 wt%) resulted in the maximum energy yield at a total flow rate of
51 mL/min. Furthermore, we investigated the relative significance of each operating parameter on the performance of the plasmacatalytic DRM process. The results show that the total flow rate had the greatest influence on the conversion, with a significance exceeding 35% for each output, while the Ni loading had the least impact on the overall reaction performance. This hybrid model demonstrates a remarkable ability to extract valuable insights from limited datasets, enabling the development and optimization of more efficient and selective plasma-catalytic chemical processes.

Abstract: Handedness is an essential attribute of chiral nanocrystals, having a major influence on their properties. During chemical growth, the handedness of nanocrystals is usually tuned by selecting the corresponding enantiomer of chiral molecules involved in asymmetric growth, often known as chiral inducers. We report that, even using the same chiral inducer enantiomer, the handedness of chiral gold nanocrystals can be reversed by using Au nanorod seeds with either single crystalline or pentatwinned structure. This effect holds for chiral growth induced both by amino acids and by chiral micelles. Although it was challenging to discern the morphological handedness for<italic>L</italic>‐cystine‐directed particles, even using electron tomography, both cases showed circular dichroism bands of opposite sign, with nearly mirrored chiroptical signatures for chiral micelle‐directed growth, along with quasi‐helical wrinkles of inverted handedness. These results expand the chiral growth toolbox with an effect that might be exploited to yield a host of interesting morphologies with tunable optical properties.

Abstract: Density functional theory is widely used to gain insights into molecule−metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA)
density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the selfinteraction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface
reaction network, namely, CO2 hydrogenation on Cu. We show that the mGGA DF used, namely, rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals and molecules, as well as molecule−metal surface adsorption
and activation energies. Hence, it is a better choice for constructing molecule−metal surface reaction networks.

Abstract: Metal–organic framework (MOF)-74 is known for its effectiveness in selectively capturing carbon dioxide (CO2). Especially the Zn and Cu versions of MOF-74 show high efficiency of this material for CO2. However, the activation of this MOF, which is a crucial step for its utilization, is so far not well understood. Here, we are closing the knowledge gap by examining the activation using, for the first time in the MOF, three-dimensional electron diffraction (3DED) during in situ heating. The use of state-of-the-art direct electron detectors enables rapid acquisition and minimal exposure times, therefore minimizing beam damage to the very electron beam-sensitive MOF material. The activation process of Zn-MOF-74 and Cu-MOF-74 is systematically studied in situ, proving the creation of open metal sites. Differences in thermal stability between Zn-MOF-74 and Cu-MOF-74 are attributed to the strength of the metal–oxygen bonds and Jahn–Teller distortions. In the case of Zn-MOF-74, we observe previously unknown remaining electrostatic potentials inside the MOF pores, which indicate the presence of remaining atoms that might impede gas flow throughout the structure when using the MOF for absorption purposes. We believe our study exemplifies the significance of employing advanced characterization techniques to enhance our material understanding, which is a crucial step for unlocking the full potential of MOFs in various applications.

Abstract: Plasma technology is gaining increasing interest for the conversion of greenhouse gases, such as CO2 and CH4, into value-added chemicals using (renewable) electricity. In this paper, we study the effect of O2 addition to the combined conversion of CO2 and CH4 in an atmospheric pressure glow discharge plasma. This process is called “oxidative CO2 reforming of methane”, and we search for the optimal gas mixing ratio in terms of conversion, energy cost, product output and plasma stability. A mixing ratio of 42.5:42.5:15 CO2/CH4/O2 yields the best performance, with a CO2 and CH4 conversion of 50 and 74%, respectively, and an energy cost as low as 2 eV molecule−1 (corresponding to 7.9 kJ L−1 and 190 kJ mol−1), i.e., clearly below the target defined to be competitive with other technologies. The syngas components (CO and H2) are the most important products, with a syngas ratio, H2/CO, being 0.8. Plasma destabilization at high CH4 fractions due to solid carbon formation is the limiting factor for further improving this syngas ratio. The solid carbon material is found to be contaminated with steel particles originating from the electrode material, rendering it unappealing as a side product. Therefore, O2 addition helps to remove the carbon formation. Besides the experiments, we developed a 2D axisymmetric fluid dynamics model, which can successfully predict the experimental trends in conversion, product composition and temperatures, while providing unique insights in the formation of CxHy species.

Abstract: Efficient methane conversion to methanol remains a significant challenge in chemical industry. This study investigates the direct oxidation of methane to methanol under mild conditions, employing a synergy of nonthermal plasma and Cu-MOR (Copper-Mordenite) catalysts. Catalytic tests demonstrate that the Cu-MOR IE-3 catalyst (i.e., prepared by three cycles of ion exchange) exhibits superior catalytic performance (with 51 % methanol selectivity and 7.9 % methane conversion). Conversely, the Cu-MOR catalysts prepared via wetness impregnation tend to over-oxidize CH4 to CO and CO2. Through systematic catalyst characterizations (XRD, TPR, UV–Vis, HRTEM, XPS), we elucidate that ion exchange mainly leads to the formation of zeolite-confined Cu2+ species, while wetness impregnation predominantly results in CuO particles. Based on the catalytic performance, catalyst characterizations and in-situ FTIR spectra, we conclude that zeolite-confined Cu2+ species serve as the active sites for plasma-catalytic direct oxidation of methane to methanol.

Abstract: Thermal plasma-driven dry reforming of methane (DRM) has gained increased attention in recent years due to its high conversion and energy conversion efficiency (ECE). Recent experimental work investigated the performance of a pure CO₂plasma with post-plasma CH₄injection. The rationale behind this strategy is that by utilizing a pure CO₂plasma, all plasma energy can be used to dissociate CO₂, while CH₄reforming proceeds post-plasma in the reforming reactor with residual heat, potentially improving the energy efficiency compared to injecting both CO₂and CH₄into the plasma. To assess whether post-plasma CH₄injection indeed improves the DRM performance, we developed a chemical kinetics model describing the post-plasma conversion process. We first validated our model by reproducing the experimental results of the pure CO₂plasma with post-plasma CH₄injection. Subsequently, we compared both strategies: injecting only CO₂inside the plasma while injecting CH₄post-plasma,<italic>vs.</italic>classical plasma-based DRM. Our modeling results indicate that below specific energy inputs (SEI) of 220 kJ mol⁻¹, the total conversion slightly improves (<italic>ca.</italic>5%) with the first strategy. However, the ECE is slightly lower due to the low H₂selectivity caused by substantial H₂O formation. The highest conversion and ECE are obtained at SEI values of 240–280 kJ mol⁻¹, where both strategies yield nearly identical results, indicating the limited potential of improving the performance of DRM by pure CO₂plasma with post-plasma CH₄injection. Nevertheless, the approach is still very valuable to allow higher CH₄/CO₂ratios without problems of coke formation within the plasma, and thus, to improve plasma stability and reach higher syngas ratios, which is more useful for further Fischer–Tropsch or methanol synthesis.

Abstract: renewable energy. Plasma technology is promising for this purpose, as it can crack NH3 without the need for a catalyst and is highly compatible with renewable electricity, reducing the environmental footprint of the cracking process. This work investigates the NH3 cracking performance of four different warm plasma reactors with different configurations and operating in a wide range of conditions. We show that the NH3 conversion in warm plasma reactors is primarily determined by the specific energy input, with the main difference observed in the energy cost (EC) of cracking. The lowest EC obtained is 146 kJ/mol but at a conversion of only 8 %. A more reasonable conversion of around 50 % yields an EC of around 200 kJ/mol in two of the reactors investigated. Plasma reactors operating at higher feed flow rates are more efficient and yield a higher H2 production rate. Our data indicate that NH3 cracking in these warm plasma reactors occurs mainly via thermal chemistry, with nonthermal plasma chemistry playing a less prominent role. NH3 decomposes not only inside the plasma core but also in a hot volume around it, which reduces the EC. Our study shows that warm plasmas are significantly more efficient for NH3 cracking than cold plasmas, even when the latter are combined with catalysts.