Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic-resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state-of-the-art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark-field scanning transmission electron microscopy enables the acquisition of ten high-resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allow resolving the real-time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions. Experimental and computational techniques are bridged to unveil atomic dynamics in gold nanoparticles (NPs), using annular dark-field scanning transmission electron microscopy and molecular dynamics simulations informed by machine learning. The approach provides unprecedented insights into the real-time structural behaviors of NPs, merging state-of-the-art techniques to accurately characterize their dynamics under realistic conditions. image

Abstract: Activated carbon (AC) materials from renewable sources are widely used in electrochemical applications due to their well‐known high surface area. However, their application as electrode material in double‐layer electrochemical devices may be limited due to their relatively low electrical conductivity and lightweight. To overcome these limitations, the incorporation of pseudocapacitance metal oxide nanoparticles is an optimum approach. These nanoparticles can provide a second energy storage mechanism to the composite, mitigating the loss of surface area associated with their incorporation. As a result, the composite material is endowed with increased conductivity and higher density, making it more suitable for practical implementation in real devices. In this study, we have incorporated a fine dispersion of 1 % of MnO clusters into a highly porous activated carbon synthesized from walnut shells (WAC). The high‐resolution electron microscopy studies, combined with their related analytical techniques, allow us to determine the presence of the cluster within the matrix carbon precisely. The resulting MnO@WAC composite demonstrated significantly improved capacitive behavior compared with the WAC material, with increased volumetric capacitance and higher charge retention at higher current densities. The composite‘s electrochemical performance suggests its potential as a promising electrode material for supercapacitors, addressing drawbacks associated with traditional AC materials.

Abstract: We investigate the intralayer correlations acting within the layers in a superfluid system of electron -hole spatially separated layers. In this system, superfluidity is predicted to be almost exclusively confined to the Bose-Einstein condensate (BEC) and crossover regimes where the electron -hole pairs are well localized. In this case, Hartree-Fock is an excellent approximation for the intralayer correlations. We find in the BEC regime that the effect of the intralayer correlations on superfluid properties is negligible but in the BCS-BEC crossover regime the superfluid gap is significantly weakened by the intralayer correlations. This is caused by the intralayer correlations boosting the number of low -energy particle -hole excitations that drive the screening. We further find that the intralayer correlations suppress the predicted phenomenon in which the average pair size passes through a minimum as the crossover regime is traversed.

Abstract: The emerging reductive catalytic fractionation biorefinery which is currently under development aims to convert woody biomass efficiently into high-value products. Despite its potential, the environmental consequences of its implementation are not well known. Therefore, a forward-looking consequential life cycle assessment examines greenhouse gas emissions associated with its products (pulp, phenolic monomers, and oligomers) compared to alternative market options. Findings indicate that current greenhouse gas emissions exceed those of the existing alternatives, with by-products and the gaseous waste stream as major contributors. Process adaption to (i) produce higher-valued products (bleached pulps, phenols, and propylene) and (ii) incinerate gaseous waste stream for energy are proposed, potentially reducing emissions by up to 50 %, outperforming alternative options. Compared to land-based transportation, waterways can increase feedstock availability by up to 1000 km without an increase in emissions. In conclusion, the consequential approach provides valuable insights for enhancing and optimizing the environmental performance of the process.

Abstract: The interplay between a magnetic field and the Coulomb potential from a charged vacancy on the electron states in graphene is investigated within the tight-binding model. The Coulomb potential removes locally Landau level degeneracy, while the vacancy introduces a satellite level next to the normal Landau level. These satellite levels are found throughout the positive-energy region, but in the negative-energy region, they turn into atomic collapse resonances. Crossings between Landau levels with different angular quantum number m are found. Unlike the point impurity system in which an anticrossing occurs between Landau levels of the same m, in this work anticrossing is found between the normal Landau level and the vacancy-induced level. The atomic collapse resonance hybridizes with the Landau levels. The charge at which the lowest Landau level m = -1, N = 1 crosses E = 0 increases with enhancing magnetic field. A Landau level scaling anomaly occurs when the charge is larger than the critical charge beta 0.6 and this critical charge is independent of the magnetic field.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image

Abstract: Oligomerized [Cu-O-Cu] species are reported to be efficient in promoting plasma catalytic one-step steam reforming of methane to methanol and hydrogen, achieving 6.8% CH4 conversion and 73.1% CH3OH selectivity without CO2.

Abstract: Nitrogen-doped diamond (N-D) is one of the most important carbon-based electronic and optical materials. Here we study the terahertz (THz) magneto-optical (MO) properties of N-D grown by microwave plasma-enhanced chemical vapor deposition. The optical microscope, SEM, XRD, Raman spectrum, FTIR spectroscopy and XPS are used for the characterization of N-D samples. Applying THz time-domain spectroscopy (TDS), in combination with the polarization test and the presence of magnetic field in Faraday geometry, THz MO transmissions through N-D are measured from 0 to 8 T at 80 K. The complex right- and left-handed circular transmission coefficients and MO conductivities for N-D are obtained accordingly. Through fitting the experimental results with theoretical formulas of the dielectric constant and MO conductivities for an electron gas, we are able to determine magneto-optically the key electronic parameters of N-D, such as the static dielectric constant epsilon b, the electron density ne, the electronic relaxation time tau, the electronic localization factor alpha and, particularly, the effective electron mass m* obtained under non-resonant condition. The dependence of these parameters upon magnetic field is examined and analyzed. We find that the MO conductivities of N-D can be described rightly by the MO Drude-Smith formulas developed by us previously. It is shown that N-doping and the presence of the magnetic field can lead towards the larger epsilon b and heavier m* in diamond, while ne/tau/alpha in N-D decreases/increases/decreases with increasing magnetic field. The results obtained from this work are benefit to us in gaining an in-depth understanding of the electronic and optoelectronic properties of N-D.

Abstract: We present an investigation of the microstructure changes in V2O3 as it goes through its inherent structural phase transition. Using V2O3 films with a well-defined crystal structure deposited by reactive magnetron sputtering on r-plane Al2O3 substrates, we study the phase coexistence region and its impact on the surface roughness of the films and the magnetic properties of overlying Ni magnetic layers in V2O3/Ni hybrid magnetic heterostructures. The simultaneous presence of two phases in V2O3 during its structural phase transition was identified with high resolution x-ray diffraction and led to an increase in surface roughness observed using x-ray reflectivity. The roughness reaches its maximum at the midpoint of the transition. In V2O3/Ni hybrid heterostructures, we find a concomitant increase in the coercivity of the magnetic layer correlated with the increased roughness of the V2O3 surface. The chemical homogeneity of the V2O3 is confirmed through transmission electron microscopy analysis. High-angle annular dark field imaging and electron energy loss spectroscopy reveal an atomically flat interface between Al2O3 and V2O3, as well as a sharp interface between V2O3 and Ni.

Abstract: Hydrogen (H2) is expected to become a key molecule in the transition towards a society running on renewable energy. It can be used to store excess renewable energy at peak production moments and release this energy at a later stage when renewable energy production is less. However, storing H2 is challenging due to the low density of this gas. As a solution, Liquid Organic Hydrogen Carriers or LOHC molecules have been proposed in the passed to increase volumetric energy density of H2. LOHC are a class of molecules that have storage sites available, to which the H2 gas can be chemically bounded. The LOHC molecule under investigation was dibenzyltoluene (DBT), which is an oil like liquid, that is easy to transport and poses little fire or explosion risks. To release the H2 from the DBT carrier, via a so-called dehydrogenation reaction, efficient mass and heat transfer is required during the process, since a large volume increase is expected from H2 release and the reaction is endothermic, i.e., a self – cooling process that takes place at temperatures around 300 C. The heat has to be supplied specifically to the active sites of catalyst particles that are present inside the reactor and which enable the dehydrogenation to proceed. For heat transfer limited processes fluidized bed reactors are often used, which is a type of reactor where the particle phase is being agitated by the fluid flow. The research proposed in this work, was to explore via computational fluid dynamics (CFD) simulations the possibilities and challenges of using fluidized bed reactors for the dehydrogenation of LOHC. The model selection required for CFD simulations of a three-phase system was investigated in this work, with a main emphasis on the drag model selection. The CFD modelling study was focused on the use of swirling fluidized bed reactors, since it was hypothesised that the swirling effect could also aid in increased removal of the gas phase from the reaction medium to increase the efficiency of the process. Ultimately, it was shown that the main challenges in the design of fluidized bed reactors will be to create uniform particle distribution inside the reactor. A new design for a dehydrogenation reactor is proposed based on the insights gained in this thesis.

Abstract: This study introduces solid-state tuning of a mesostructured cellular foam (MCF) to enhance hydrogen (H-2) storage in clathrate hydrates. Grafting of promoter-like molecules (e.g., tetrahydrofuran) at the internal surface of the MCF resulted in a substantial improvement in the kinetics of formation of binary H-2-THF clathrate hydrate. Identification of the confined hydrate as sII clathrate hydrate and enclathration of H-2 in its small cages was performed using XRD and high-pressure H-1 NMR spectroscopy respectively. Experimental findings show that modified MCF materials exhibit a similar to 1.3 times higher H-2 storage capacity as compared to non-modified MCF under the same conditions (7 MPa, 265 K, 100% pore volume saturation with a 5.56 mol% THF solution). The enhancement in H-2 storage is attributed to the hydrophobicity originating from grafting organic molecules onto pristine MCF, thereby influencing water interactions and fostering an environment conducive to H-2 enclathration. Gas uptake curves indicate an optimal tuning point for higher H-2 storage, favoring a lower density of carbon per nm(2). Furthermore, a direct correlation emerges between higher driving forces and increased H-2 storage capacity, culminating at 0.52 wt% (46.77 mmoles of H-2 per mole of H2O and 39.78% water-to-hydrate conversions) at 262 K for the modified MCF material with fewer carbons per nm(2). Notably, the substantial H-2 storage capacity achieved without energy-intensive processes underscores solid-state tuning's potential for H-2 storage in the synthesized hydrates. This study evaluated two distinct kinetic models to describe hydrate growth in MCF. The multistage kinetic model showed better predictive capabilities for experimental data and maintained a low average absolute deviation. This research provides valuable insights into augmenting H-2 storage capabilities and holds promising implications for future advancements.

Abstract: Even at low temperatures, metal nanoparticles (NPs) possess atomic dynamics that are key for their properties but challenging to elucidate. Recent experimental advances allow obtaining atomic‐resolution snapshots of the NPs in realistic regimes, but data acquisition limitations hinder the experimental reconstruction of the atomic dynamics present within them. Molecular simulations have the advantage that these allow directly tracking the motion of atoms over time. However, these typically start from ideal/perfect NP structures and, suffering from sampling limits, provide results that are often dependent on the initial/putative structure and remain purely indicative. Here, by combining state‐of‐the‐art experimental and computational approaches, how it is possible to tackle the limitations of both approaches and resolve the atomistic dynamics present in metal NPs in realistic conditions is demonstrated. Annular dark‐field scanning transmission electron microscopy enables the acquisition of ten high‐resolution images of an Au NP at intervals of 0.6 s. These are used to reconstruct atomistic 3D models of the real NP used to run ten independent molecular dynamics simulations. Machine learning analyses of the simulation trajectories allows resolving the real‐time atomic dynamics present within the NP. This provides a robust combined experimental/computational approach to characterize the structural dynamics of metal NPs in realistic conditions.

Abstract: In nature, the two-dimensional (2D) profiles of fruits from many plants often resemble ellipses. However, it remains unclear whether these profiles strictly adhere to the ellipse equation, as many natural shapes resembling ellipses are actually better described as superellipses. The superellipse equation, which includes an additional parameter n compared to the ellipse equation, can generate a broader range of shapes, with the ellipse being just a special case of the superellipse. To investigate whether the 2D profiles of fruits are better described by ellipses or superellipses, we collected a total of 751 mature and undamaged fruits from 31 naturally growing plants of Cucumis melo L. var. agrestis Naud. Our analysis revealed that most adjusted root-mean-square errors (> 92% of the 751 fruits) for fitting the superellipse equation to the fruit profiles were consistently less than 0.0165. Furthermore, there were 638 of the 751 fruits (ca. 85%) with the 95% confidence intervals of the estimated parameter n in the superellipse equation not including 2. These findings suggest that the profiles of C. melo var. agrestis fruits align more closely with the superellipse equation than with the ellipse equation. This study provides evidence for the existence of the superellipse in fruit profiles, which has significant implications for studying fruit geometries and estimating fruit volumes using the solid of revolution formula. Furthermore, this discovery may contribute to a deeper understanding of the mechanisms driving the evolution of fruit shapes.

Abstract: Coupling photosensitizers (PSs) with plasmonic nanoparticles increases the photocatalytic activity of PSs as the localized surface plasmon resonance (LSPR) of plasmonic nanoparticles leads to extreme concentration of light in their vicinity known as the near-field enhancement effect. To realize this in a colloidal phase, efficient conjugation of the PS molecules with the plasmonic nanoparticle surface is critical. In this work, we demonstrate the coupling of phthalocyanine (Pc) molecules with gold nanoparticles (AuNPs) in the colloidal phase via click chemistry. This conjugated Pc-AuNPs colloidal system is shown to enhance the photocatalytic singlet oxygen (1O2) production over non-conjugated Pcs and hence improve the photoelectrochemical detection of phenols. The plasmonic enhancement of the 1O2 generation by Pcs was clearly elucidated by complementary experimental and computational classical electromagnetic models. The dependence of plasmonic enhancement on the spectral position of the excitation laser wavelength and the absorbance of the Pc molecules with respect to the wavelength specific near-field enhancement is clearly demonstrated. A high similar to 8 times enhancement is obtained with green laser (532 nm) at the LSPR due to the maximum near-field enhancement at the resonance wavelength. Zinc phthalocyanine is covalently linked to plasmonic AuNPs via click chemistry to investigate the synergistic effect that boosts the overall activity toward the detection of HQ under visible light illumination. The 1O2 quantum yield of ZnPc improved significantly after conjugating with AuNPs, resulting in enhanced photoelectrochemical activity. image

Abstract: A thorough and quantitative understanding of the fate of excitons in covalent–organic frameworks (COFs) after photoexcitation is essential for their augmented optoelectronic and photocatalytic applications via precise structure tuning. The synthesis of a library of COFs having identical chemical backbone with impeded conjugation, but varied morphology and surface topography to study the effect of these physical properties on the photophysics of the materials is herein reported. The variation of crystallite size and surface topography substantified different aggregation pattern in the COFs, which leads to disparities in their photoexcitation and relaxation properties. Depending on aggregation, an inverse correlation between bulk luminescence decay time and exciton binding energy of the materials is perceived. Further transient absorption spectroscopic analysis confirms the presence of highly localized, immobile, Frenkel excitons (of diameter 0.3–0.5 nm) via an absence of annihilation at high density, most likely induced by structural torsion of the COF skeletons, which in turn preferentially relaxes via long‐lived (nanosecond to microsecond) excimer formation (in femtosecond scale) over direct emission. These insights underpin the importance of structural and topological design of COFs for their targeted use in photocatalysis.