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: Several structures and electronic states of the C2N and CN2 molecules have been studied using complete active space SCF (CASSCF), multireference configuration interaction (MRCI), and coupled cluster (CCSD(T)) methods. Both molecules are very stable. Our best computed total atomization energies SIGMAD(e) are 288.6 +/- 2 kcal/mol for CN2, and 294.1 +/- 2 kcal/mol for C2N. The CNC and CCN structures for C2N are nearly isoenergetic. CNN(3PI) lies about 30 kcal/mol above NCN(3PI(g)), but has a high barrier towards interconversion and is therefore observed experimentally. Computed harmonic frequencies for CNN are sensitive to the correlation treatment: they are reproduced well using multireference methods as well as the CCSD(T) method. High spin contamination has a detrimental effect on computed harmonic frequencies at the CCSD(T) level.

Abstract: Understanding the fundamental dynamics in carbon nanotube (CNT) catalysts is of primary importance to understand CNT nucleation. This Letter reports on calculated bond switching (BS) rates in pure and carbon containing nickel nanoclusters. The rates are analyzed in terms of their temperature dependent spatial distribution and the mobility of the cluster atoms. The BS mechanism is found to change from vibrational to diffusional at around 900 K, with a corresponding strong increase in activation energy. Furthermore, the BS activation energy is observed to decrease as the carbon content in the cluster increases, resulting in an effective liquification of the cluster.

Abstract: The mechanism allowing the transition from a two-dimensional strained layer of CdSe on ZnSe to self-assembled islands induced by the use of amorphous selenium is still not fully understood. For a better understanding, atomic force microscopy and transmission electron microscopy studies were performed on CdSe films with a thickness close to that for quantum dot formation. Below this thickness, the sample surface results in undulations along the [110] crystal direction, while few quantum dots are situated in the wave valleys. Plan view transmission electron microscopy studies reveal a strong anisotropy of the islands and show that the Se desorption conditions are crucial. (C) 2006 Elsevier B.V. All rights reserved.

Abstract: Multi-wall carbon nanotubes have been produced by the catalytic decomposition of acetylene. Go-Mo, Co-V and Co-Fe mixtures supported either on zeolite or corundum alumina were used as catalysts. When Fe or V is added to Co, the carbon deposit increases. The nanotubes were characterized by both low and high resolution TEM. From histograms representing the outer diameter distributions, it is clear that the outer diameter of the nanotubes can be controlled by choosing the appropriate catalyst. (C) 2000 Elsevier Science B.V. All rights reserved.

Abstract: A far-infrared absorption study of electrons in lightly-doped GaAs/Al0.3Ga0.7As superlattices is presented. Both weakly and strongly coupled superlattices are investigated, and the difference between intersubband transitions and transitions between extended minibands is demonstrated. At low temperatures, the absorption spectra are dominated by donor transitions. The 1s-2p(z) transition, which is intimately related to the intersubband transition, is observed. All experimental data are compared to an envelope function calculation for the miniband structure and a variational calculation for the donor energies. Excellent agreement between experiment and theory is achieved.