Extremely Stable Ag-Based Photonics, Plasmonic, Optical, and Electronic Materials and Devices Designed with Surface Chemistry Engineering for Anti-Tarnish.

Silver (Ag) metal-based structures are promising building blocks for next-generation photonics and electronics owing to their unique characteristics, such as high reflectivity, surface plasmonic resonance effects, high electrical conductivity, and tunable electron transport mechanisms. However, Ag structures exhibit poor sustainability in terms of device performance because harsh chemicals, particularly S2- ions present in the air, can damage their structures, lowering their optical and electrical properties. Here, the surface chemistry of Ag structures with (3-mercaptopropyl)trimethoxysilane (MPTS) ligands at room temperature and under ambient conditions is engineered to prevent deterioration of their optical and electrical properties owing to S2- exposure. Regardless of the dimensions of the Ag structures, the MPTS ligands can be applied to each dimension (0D, 1D, and 3D). Consequently, highly sustainable plasmonic effects (Δλ < 2 nm), Fabry-Perot cavity resonance structures (Δλ < 2 nm), reflectors (ΔRReflectance < 0.5%), flexible electrodes (ΔRelectrical < 0.1 Ω), and strain gauge sensors (ΔGF < 1), even in S2- exposing conditions is achieved. This strategy is believed to significantly contribute to environmental pollution reduction by decreasing the volume of electronic waste.

Monocular depth estimation network with single-pixel depth guidance.

Due to the scale ambiguity problem, the performance of monocular depth estimation (MDE) is inherently restricted. Multi-camera systems, especially those equipped with active depth cameras, have addressed this problem at the expense of increased hardware costs and space. In this Letter, we adopt a similar but cost-effective solution using only single-pixel depth guidance with a single-photon avalanche diode. To this end, we design a single-pixel guidance module (SPGM) that combines the global information from the single-pixel depth guidance with the spatial information from the image at the feature level. By integrating SPGMs into an MDE network, we introduce PhoMoNet, the first, to the best of our knowledge, end-to-end MDE network with single-pixel depth guidance. Experimental results show the effectiveness and superiority of PhoMoNet over state-of-the-art MDE networks on synthetic and real-world datasets.

Design of Eu(TTA)3phen-incorporated SiO2-coated transition metal oxide nanoparticles for efficient luminescence and magnetic performance.

The development of multifunctional nanoparticles (NPs) combining individual properties, such as magnetic, luminescence, and optical properties, has attracted significant research interest. In this study, europium (Eu)-incorporating iron oxide nanoparticles (IONPs) with Eu(TTA)3phen (ET-SIOPs) were successfully designed and shown to have luminescence and magnetic properties. The proposed synthetic method has three steps: (1) IONP synthesis, (2) SiO2 layer coating (1st coating), and (3) Eu-SiO2 layer coating (2nd coating). The morphology of the ET-SIOPs was well preserved after the 2nd coating was conducted. According to the photoluminescence (PL) spectra in the range of 500 to 700 nm, the Eu-incorporating SIOPs with Eu(TTA)3phen (ET-SIOPs) exhibited the highest emission intensity compared to the Eu-incorporating SIOPs synthesized with other Eu precursors. Furthermore, the ET-SIOPs exhibited long-term luminescence stability of 6 months. In addition, this method of double-layer coating can be applied to other materials synthesized with different compositions and shapes, such as MnO and SiO2 NPs. The findings of this study will not only provide new insights for the synthesis of luminescent-magnetic NPs with long-term luminescence stability and paramagnetic properties, but can also be applied for the design of various multifunctional NPs.

How Do Colloidal Nanoparticles Move in a Solution under an Electric Field?: In Situ Light Scattering Analysis.

Understanding the dynamics of colloidal nanoparticles (NPs) in a solution is the key to assembling them into solids through a solution process such as electrophoretic deposition. In this study, newly developed in situ analysis with light scattering is used to examine NP dynamics induced by a non-uniform electric field. We reveal that the symmetric directions of moving NP aggregates are due to dielectrophoresis between the cylindrical electrodes, while the actual NP deposition is based on the charge of NPs (electrophoresis). Over time, the symmetry of the dynamics becomes less evident, inducing feeble deposition as the less-ordered dynamics become stronger. Eventually, two separate deposition mechanisms emerge as the deposition rate decreases with the change in the NP dynamics. Furthermore, we identify the vortex-like NP motion between the electrodes. These in situ analyses provide insights into the electrophoretic deposition mechanism and NP behavior in a solution under an electric field for fine film construction.

Contact Enhancement in Nanoparticle Assemblies through Electrophoretic Deposition.

A strong interparticle connection needs to be realized to harvest unique nanoscale features of colloidal nanoparticles (NPs) in film structures. Constructing a strong contact and adhesion of NPs on a substrate is an essential process for improved NP film properties, and therefore, its key factors should be determined by understanding the NP deposition mechanism. Herein, we investigated the critical factors leading to the robust and strong adherence of the film structure and revealed that the NP deposition mechanism involved the role of surfactant ligands during electrophoretic deposition (EPD). The high amount of surfactant ligand treatment results in a high deposition rate of NPs in the early stage; however, the ligand treatment does not influence the deposition rate in the later stage. Furthermore, the deposition mechanism is found to involve three steps during EPD: island formation, lateral growth, and layer-by-layer deposition. Rapid NP deposition kinetics controlled by ligand treatments demonstrate the strong contact and adhesion of NP film structures; they are characterized by the fast charge transfer, low resistivity, and rigid NP layers of the Cu2-x S NP-based devices. Finally, the controlled role of surfactant ligands in EPD enables design of high-performance nanostructured NP film devices with contact enhancement.

Leveraging Nitrogen Linkages in the Formation of a Porous Thorium-Organic Nanotube Suitable for Iodine Capture.

We report the synthesis, characterization, and iodine capture application of a novel thorium-organic nanotube, TSN-626, [Th6O4(OH)4(C6H4NO2)7(CHO2)5(H2O)3]·3H2O. The classification as a metal-organic nanotube (MONT) distinguishes it as a rare and reduced dimensionality subset of metal-organic frameworks (MOFs); the structure is additionally hallmarked by low node connectivity. TSN-626 is composed of hexameric thorium secondary building units and mixed O/N-donor isonicotinate ligands that demonstrate selective ditopicity, yielding both terminating and bridging moieties. Because hard Lewis acid tetravalent metals have a propensity to bind with electron donors of rival hardness (e.g., carboxylate groups), such Th-N coordination in a MOF is uncommon. However, the formation of key structural Th-N bonds in TSN-626 cap some of the square antiprismatic metal centers, a position usually occupied by terminal water ligands. TSN-626 was characterized by using complementary analytical and computational techniques: X-ray diffraction, vibrational spectroscopy, N2 physisorption isotherms, and density functional theory. TSN-626 satisfies design aspects for the chemisorption of iodine. The synergy between accessibility through pores, vacancies at the metal-oxo nodes, and pendent N-donor sites allowed a saturated iodine loading of 955 mg g-1 by vapor methods. The crystallization of TSN-626 diversifies actinide-MOF linker selection to include soft electron donors, and these Th-N linkages can be leveraged for the investigation of metal-to-ligand bonding and unconventional topological expressions.

Active Learning Configuration Interaction for Excited-State Calculations of Polycyclic Aromatic Hydrocarbons.

We present the active learning configuration interaction (ALCI) method for multiconfigurational calculations based on large active spaces. ALCI leverages the use of an active learning procedure to find important electronic configurations among the full configurational space generated within an active space. We tested it for the calculation of singlet-singlet excited states of acenes and pyrene using different machine learning algorithms. The ALCI method yields excitation energies within 0.2-0.3 eV from those obtained by traditional complete active-space configuration interaction (CASCI) calculations (affordable for active spaces up to 16 electrons in 16 orbitals) by including only a small fraction of the CASCI configuration space in the calculations. For larger active spaces (we tested up to 26 electrons in 26 orbitals), not affordable with traditional CI methods, ALCI captures the trends of experimental excitation energies. Overall, ALCI provides satisfactory approximations to large active-space wave functions with up to 10 orders of magnitude fewer determinants for the systems presented here. These ALCI wave functions are promising and affordable starting points for the subsequent second-order perturbation theory or pair-density functional theory calculations.

Evolution of water structures in metal-organic frameworks for improved atmospheric water harvesting.

Although the positions of water guests in porous crystals can be identified, determination of their filling sequence remains challenging. We deciphered the water-filling mechanism for the state-of-the-art water-harvesting metal-organic framework MOF-303 by performing an extensive series of single-crystal x-ray diffraction measurements and density functional theory calculations. The first water molecules strongly bind to the polar organic linkers; they are followed by additional water molecules forming isolated clusters, then chains of clusters, and finally a water network. This evolution of water structures led us to modify the pores by the multivariate approach, thereby precisely modulating the binding strength of the first water molecules and deliberately shaping the water uptake behavior. This resulted in higher water productivity, as well as tunability of regeneration temperature and enthalpy, without compromising capacity and stability.

Electrophoretic Deposition of Aged and Charge Controlled Colloidal Copper Sulfide Nanoparticles.

Colloidal nanoparticles (NPs) have been recently spotlighted as building blocks for various nanostructured devices. Their collective properties have been exhibited by arranging them on a substrate to form assembled NPs. In particular, electrophoretic deposition (EPD) is an emerging fabrication method for such nanostructured films. To maximize the benefits of this method, further studies are required to fully elucidate the key parameters that influence the NP deposition. Herein, two key parameters are examined, namely: (i) the aging of colloidal NPs and (ii) the charge formation by surface ligands. The aging of Cu2-xS NPs changes the charge states, thus leading to different NP deposition behaviors. The SEM images of NP films, dynamic light scattering, and zeta potential results demonstrated that the charge control and restoration of interparticle interactions for aged NPs were achieved via simple ligand engineering. The charge control of colloidal NPs was found to be more dominant than the influence of aging, which can alter the surface charges of the NPs. The present results thus reveal that the charge formation on the colloidal NPs, which depends on the surface ligands, is an important controllable parameter in EPD.

Advancement of Actinide Metal-Organic Framework Chemistry via Synthesis of Pu-UiO-66.

We report the synthesis and characterization of the first plutonium metal-organic framework (MOF). Pu-UiO-66 expands the established UiO-66 series, which includes transition metal, lanthanide, and early actinide elements in the hexanuclear nodes. The thermal stability and porosity of Pu-UiO-66 were experimentally determined, and multifaceted computational methods were used to corroborate experimental values, examine inherent defects in the framework, decipher spectroscopic signatures, and elucidate the electronic structure. The crystallization of a plutonium chain side product provides direct evidence of the competition that occurs between modulator and linker in MOF syntheses. Ultimately, the synthesis of Pu-UiO-66 demonstrates adept control of Pu(IV) coordination under hydrolysis-prone conditions, provides an opportunity to extend trends across isostructural UiO-66 frameworks, and serves as the foundation for future plutonium MOF chemistry.

Automation of Active Space Selection for Multireference Methods via Machine Learning on Chemical Bond Dissociation.

Predicting and understanding the chemical bond is one of the major challenges of computational quantum chemistry. Kohn-Sham density functional theory (KS-DFT) is the most common method, but approximate density functionals may not be able to describe systems where multiple electronic configurations are equally important. Multiconfigurational wave functions, on the other hand, can provide a detailed understanding of the electronic structures and chemical bonds of such systems. In the complete active space self-consistent field (CASSCF) method, one performs a full configuration interaction calculation in an active space consisting of active electrons and active orbitals. However, CASSCF and its variants require the selection of these active spaces. This choice is not black box; it requires significant experience and testing by the user, and thus active space methods are not considered particularly user-friendly and are employed only by a minority of quantum chemists. Our goal is to popularize these methods by making it easier to make good active space choices. We present a machine learning protocol that performs an automated selection of active spaces for chemical bond dissociation calculations of main group diatomic molecules. The protocol shows high prediction performance for a given target system as long as a properly correlated system is chosen for training. Good active spaces are correctly predicted with a considerably better success rate than random guess (larger than 80% precision for most systems studied). Our automated machine learning protocol shows that a "black-box" mode is possible for facilitating and accelerating the large-scale calculations on multireference systems where single-reference methods such as KS-DFT cannot be applied.

Modeling adsorption properties of structurally deformed metal-organic frameworks using structure-property map.

Structural deformation and collapse in metal-organic frameworks (MOFs) can lead to loss of long-range order, making it a challenge to model these amorphous materials using conventional computational methods. In this work, we show that a structure-property map consisting of simulated data for crystalline MOFs can be used to indirectly obtain adsorption properties of structurally deformed MOFs. The structure-property map (with dimensions such as Henry coefficient, heat of adsorption, and pore volume) was constructed using a large data set of over 12000 crystalline MOFs from molecular simulations. By mapping the experimental data points of deformed SNU-200, MOF-5, and Ni-MOF-74 onto this structure-property map, we show that the experimentally deformed MOFs share similar adsorption properties with their nearest neighbor crystalline structures. Once the nearest neighbor crystalline MOFs for a deformed MOF are selected from a structure-property map at a specific condition, then the adsorption properties of these MOFs can be successfully transformed onto the degraded MOFs, leading to a new way to obtain properties of materials whose structural information is lost.

Work function engineering of SnO single crystal microplates with thermal annealing.

We synthesized black SnO single-crystal microplates via a sonochemical process and engineered the work function of the SnO microplates using thermal treatments. The as-synthesized SnO microplates have a wide (001) plane, as is clearly evident from TEM images and diffraction patterns. Surface potential measurements on the SnO microplates show that the work function changes as the annealing temperature increases. The TEM and XAS results after thermal treatments imply that the micro-sized SnO(001) single-crystals are stable up to about 400 °C in air, after which the surface starts to become locally oxidized. Consequently, the long-range ordering and lattice parameter of the SnO(001) single crystals started to change to make polycrystalline SnO2 at about 600 °C. These results demonstrate the ability to tune the work function of the microplates and suggest an intriguing way to engineer the electrical properties of nanostructures.

Ultraflat Au nanoplates as a new building block for molecular electronics.

We demonstrate the charge transport properties of a self-assembled organic monolayer on Au nanoplates with conductive probe atomic force microscopy (CP-AFM). Atomically flat Au nanoplates, a few hundred micrometers on each side, that have only (111) surfaces, were synthesized using the chemical vapor transport method; these nanoplates were employed as the substrates for hexadecanethiol (HDT) self-assembled monolayers (SAMs). Atomic-scale high-resolution images show (√3 x √3) R30° molecular periodicity, indicating a well-ordered structure of the HDT on the Au nanoplates. We observed reduced friction and adhesion forces on the HDT SAMs on Au nanoplates, compared with Si substrates, which is consistent with the lubricating nature of HDT SAMs. The electrical properties, such as I-V characteristics and current as a function of load, were measured using CP-AFM. We obtained a tunneling decay constant (ß) of 0.57 Å(-1), including through-bond (ßtb = 0.99 Å(-1)) and through-space (ßts = 1.36 Å(-1)) decay constants for the two-pathway model. This indicates that the charge transport properties of HDT SAMs on Au nanoplates are consistent with those on a Au (111) film, suggesting that SAMs on nanoplates can provide a new building block for molecular electronics.

Effect of BCAA intake during endurance exercises on fatigue substances, muscle damage substances, and energy metabolism substances.

The increase rate of utilization of branched-chain amino acids (BCAA) by muscle is reduced to its plasma concentration during prolonged exercise leading to glycogen. BCAA supplementation would reduce the serum activities of intramuscular enzymes associated with muscle damage. To examine the effects of BCAA administration on fatigue substances (serotonin, ammonia and lactate), muscle damage substances (CK and LDH) and energy metabolism substances (FFA and glucose) after endurance exercise. Subjects (n = 26, college-aged males) were randomly divided into an experimental (n = 13, EXP) and a placebo (n = 13, CON) group. Subjects both EXP and CON performed a bout of cycle training (70% VO2max intensity) to exhaustion. Subject in the EXP were administrated BCAA (78ml/kg·w) prior to the bout of cycle exercise. Fatigue substances, muscle damage substances and energy metabolism substances were measured before ingesting BCAAs and placebos, 10 min before exercise, 30 min into exercise, immediately after exercise, and 30 min after exercise. Data were analyzed by two-way repeated measure ANCOVA, correlation and statistical significance was set at p < 0.05. The following results were obtained from this study; 1. In the change of fatigue substances : Serotonin in the EXP tended to decreased at the 10 min before exercise, 30 min into exercise, post exercise, and recovery 30 min. Serotonin in the CON was significantly greater than the EXP at the10 min before exercise and recovery 30. Ammonia in the EXP was increased at the 10 min before exercise, 30 min into exercise, and post exercise, but significantly decreased at the recovery 30min (p < 0.05). Ammonia in the CON was significantly lower than the EXP at the 10 min before exercise, 30 min into exercise, and post exercise (p < 0.05). Lactate in the EXP was significantly increased at the 30 min into exercise and significantly decreased at the post exercise and recovery 30 min. Lactate in the CON was significantly lower than the EXP at the post exercise (p < 0.05). 2. In the change of muscle damage substances : CK in the EXP was decreased at the 10 min before exercise and increased at the 30 min into exercise and then decreased at the post exercise and recovery 30 min. CK in the CON was greater than the EXP. LDH in the EXP was decreased at the 10 min before exercise and increased at the 30 min into exercise and then decreased at the post exercise and recovery 30 min. LDH in the CON was higher than the EXP. 3. In the change of energy metabolism substances :Glucose in the EXP tended to decrease at the 10 min before exercise, 30 min into exercise, post exercise and recovery 30 min. Glucose in the CON was significantly greater than the EXP at the recovery 30 min (p < .05). FFA in both EXP and CON was increased at the post exercise and recovery 30 min. % increase for FFA in the EXP was greater than the CON at the post exercise and recovery 30 min. 4. The relationship of the fatigue substances, muscle damage substances and energy metabolism substances after endurance exercise indicated strongly a positive relationship between LDH and ammonia and a negative relationship between LDH and FFA in the EXP. Also, there were a strong negative relationship between glucose and FFA and a positive relationship between glucose and serotonin in the EXP. There was a strong positive relationship between CK and LDH and a strong negative relationship between FFA and glucose in the CON. These results indicate that supplementary BCAA decreased serum concentrations of the intramuscular enzymes as CK and LDH following exhaustive exercise. This observation suggests that BCAA supplementation may reduce the muscle damage associated with endurance exercise.