Active site engineering of Zn-doped mesoporous ceria toward highly efficient organophosphorus hydrolase-mimicking nanozyme.

Hydrolase-mimicking nanozymes have received increasing attention in recent years, but the effective rational design and development of these materials has not been realized, as they are not at present considered a critical research target. Herein, we report that Zn-doped mesoporous ceria (Zn-m-ceria) engineered to have an abundance of two different active sites with different functions-one that allows both co-adsorption binding of organophosphate (OP) and water and another that serves as a general base-has significant organophosphorus hydrolase (OPH)-like catalytic activity. Specifically, Zn-m-ceria exhibits a catalytic efficiency over 75- and 25-fold higher than those of m-ceria and natural OPH, respectively. First-principles calculations reveal the importance of Zn for the OPH-mimicking activity of the material, promoting substrate adsorption and proton-binding. The OPH-like Zn-m-ceria catalyst is successfully applied to detect a model OP, methyl paraoxon, in spiked tap water samples with excellent sensitivity, stability, and detection precision. We expect that these findings will promote research based on the rational engineering of the active site of nanozymes and efficient strategies for obtaining a diverse range of catalysts that mimic natural enzymes, and hence the utilization in real-world applications of enzyme-mimicking catalysts with properties superior to their natural analogs should follow.

Method for 3D atomic structure determination of multi-element nanoparticles with graphene liquid-cell TEM.

Determining the 3D atomic structures of multi-element nanoparticles in their native liquid environment is crucial to understanding their physicochemical properties. Graphene liquid cell (GLC) TEM offers a platform to directly investigate nanoparticles in their solution phase. Moreover, exploiting high-resolution TEM images of single rotating nanoparticles in GLCs, 3D atomic structures of nanoparticles are reconstructed by a method called "Brownian one-particle reconstruction". We here introduce a 3D atomic structure determination method for multi-element nanoparticle systems. The method, which is based on low-pass filtration and initial 3D model generation customized for different types of multi-element systems, enables reconstruction of high-resolution 3D Coulomb density maps for ordered and disordered multi-element systems and classification of the heteroatom type. Using high-resolution image datasets obtained from TEM simulations of PbSe, CdSe, and FePt nanoparticles that are structurally relaxed with first-principles calculations in the graphene liquid cell, we show that the types and positions of the constituent atoms are precisely determined with root mean square displacement values less than 24 pm. Our study suggests that it is possible to investigate the 3D atomic structures of synthesized multi-element nanoparticles in liquid phase.

Tuning the Site-to-Site Interaction in Ru-M (M = Co, Fe, Ni) Diatomic Electrocatalysts to Climb up the Volcano Plot of Oxygen Electroreduction.

The modulating of the geometric and electronic structures of metal-N-C atomic catalysts for improving their performance in catalyzing oxygen reduction reactions (ORRs) is highly desirable yet challenging. We herein report a delicate "encapsulation-substitution" strategy for the synthesis of paired metal sites in N-doped carbon. With the regulation of the d-orbital energy level, a significant increment in oxygen electroreduction activity was demonstrated in Ru-Co diatomic catalyst (DAC) compared with other diatomic (Ru-Fe and Ru-Ni) and single-atomic counterparts. The Ru-Co DAC efficiently reduces oxygen with a halfwave potential of 0.895 V vs RHE and a turnover frequency of 2.424 s-1 at 0.7 V, establishing optimal thermodynamic and kinetic behaviors in the triple-phase reaction under practical conditions. Moreover, the Ru-Co DAC electrode displays bifunctional activity in a gas diffusion Zn-air battery with a small voltage gap of 0.603 V, outperforming the commercial Pt/C|RuO2 catalyst. Our findings provide a clear understanding of site-to-site interaction on ORR and a benchmark evaluation of atomic catalysts with correlations of diatomic structure, energy level, and overall catalytic performance at the subnanometer level.

Metastable hexagonal close-packed palladium hydride in liquid cell TEM.

Metastable phases-kinetically favoured structures-are ubiquitous in nature1,2. Rather than forming thermodynamically stable ground-state structures, crystals grown from high-energy precursors often initially adopt metastable structures depending on the initial conditions, such as temperature, pressure or crystal size1,3,4. As the crystals grow further, they typically undergo a series of transformations from metastable phases to lower-energy and ultimately energetically stable phases1,3,4. Metastable phases sometimes exhibit superior physicochemical properties and, hence, the discovery and synthesis of new metastable phases are promising avenues for innovations in materials science1,5. However, the search for metastable materials has mainly been heuristic, performed on the basis of experiences, intuition or even speculative predictions, namely 'rules of thumb'. This limitation necessitates the advent of a new paradigm to discover new metastable phases based on rational design. Such a design rule is embodied in the discovery of a metastable hexagonal close-packed (hcp) palladium hydride (PdHx) synthesized in a liquid cell transmission electron microscope. The metastable hcp structure is stabilized through a unique interplay between the precursor concentrations in the solution: a sufficient supply of hydrogen (H) favours the hcp structure on the subnanometre scale, and an insufficient supply of Pd inhibits further growth and subsequent transition towards the thermodynamically stable face-centred cubic structure. These findings provide thermodynamic insights into metastability engineering strategies that can be deployed to discover new metastable phases.

Two-Dimensional Palladium Phosphoronitride for Oxygen Reduction.

Two-dimensional (2D) catalysts often show extraordinary activity at low mass loading since almost all their atoms are exposed to electrolyte. Palladium (Pd) holds great promise for catalyzing oxygen reduction reaction (ORR) but 2D Pd-based ORR catalyst has rarely been reported. Herein, 2D ternary palladium phosphoronitride (Pd3P2Nx) is synthesized, for the first time, for ORR catalysis. The synthesis is guided by a rational design using first-principles density functional theory calculations, and then realized via a postsynthesis substitutional doping of ternary palladium thiophosphate (Pd3P2S8), which almost completely replaces sulfur atoms by nitrogen atoms without destroying the 2D morphology. The doping process exposes the interlocked Pd atoms of Pd3P2S8 and introduces ligands that improve the affinity of oxygen intermediates, resulting in greater kinetics and lower activation energy for ORR. The mass activity of the pristine Pd3P2S8 is dramatically increased as much as 5-fold (from 0.03 to 0.151 mA µg-1 Pd in Pd3P2Nx). The ORR diffusion-limited current density of Pd3P2Nx (6.2 mA cm-2) exceeds that of commercial Pt/C, and it shows fast kinetics and robust long-term stability. Our theoretical calculations not only guide the experimental doping process, but also provides insights into the underlying mechanism of the outstanding ORR activity and stability.

First-Principles-Based Machine-Learning Molecular Dynamics for Crystalline Polymers with van der Waals Interactions.

Machine-learning (ML) techniques have drawn an ever-increasing focus as they enable high-throughput screening and multiscale prediction of material properties. Especially, ML force fields (FFs) of quantum mechanical accuracy are expected to play a central role for the purpose. The construction of ML-FFs for polymers is, however, still in its infancy due to the formidable configurational space of its composing atoms. Here, we demonstrate the effective development of ML-FFs using kernel functions and a Gaussian process for an organic polymer, polytetrafluoroethylene (PTFE), with a data set acquired by first-principles calculations and ab initio molecular dynamics (AIMD) simulations. Even though the training data set is sampled only with short PTFE chains, structures of longer chains optimized by our ML-FF show an excellent consistency with density functional theory calculations. Furthermore, when integrated with molecular dynamics simulations, the ML-FF successfully describes various physical properties of a PTFE bundle, such as a density, melting temperature, coefficient of thermal expansion, and Young's modulus.

Optical bioelectronic nose of outstanding sensitivity and selectivity toward volatile organic compounds implemented with genetically engineered bacteriophage: Integrated study of multi-scale computational prediction and experimental validation.

Genetic engineering of a bacteriophage is a promising way to develop a highly functional biosensor. Almost countless configurational degree of freedom in the manipulation, considerable uncertainty and cost involved with the approach, however, have been huddles for the objective. In this paper, we demonstrate rapidly responding optical biosensor with high selectivity toward gaseous explosives with genetically engineered phages. The sensors are equipped with peptide sequences in phages optimally interacting with the volatile organic compounds (VOCs) in visible light regime. To overcome the conventional issues, we use extensive utilization of empirical calculations to construct a large database of 8000 tripeptides and screen the best for electronic nose sensing performance toward nine VOCs belonging to three chemical classes. First-principles density functional theory (DFT) calculations unveil underlying correlations between the chemical affinity and optical property change on an electronic band structure level. The computational outcomes are validated by in vitro experimental design and testing of multiarray sensors using genetically modified phage implemented with five selected tripeptide sequences and wild-type phages. The classification success rates estimated from hierarchical cluster analysis are shown to be very consistent with the calculations. Our optical biosensor demonstrates a 1 ppb level of sensing resolution for explosive VOCs, which is a substantial improvement over conventional biosensor. The systematic interplay of big data-based computational prediction and in situ experimental validation can provide smart design principles for unconventionally outstanding biosensors.

Unique design of superior metal-organic framework for removal of toxic chemicals in humid environment via direct functionalization of the metal nodes.

Unique chemical and thermal stabilities of a zirconium-based metal-organic framework (MOF) and its functionalized analogues play a key role to efficiently remove chemical warfare agents (ex., cyanogen chloride, CNCl) and simulant (dimethyl methylphosphonate, DMMP) as well as industrial toxic gas, ammonia (NH3). Herein, we for the first time demonstrate outstanding performance of MOF-808 for removal of toxic chemicals in humid environment via special design of functionalization of hydroxo species bridging Zr-nodes using a triethylenediamine (TEDA) to form ionic frameworks by gas phase acid-base reactions. In situ experimental analyses and first-principles density functional theory calculations unveil underlying mechanism on the selective deposition of TEDA on the Zr-bridging hydroxo sites (µ3-OH) in Zr-MOFs. The crystal structure of TEDA-grafted MOF-808 was confirmed using synchrotron X-ray powder diffraction (SXRPD). Furthermore, operando FT-IR spectra elucidate why the TEDA-grafted MOF-808 shows by far superior sorption efficiency to other MOF varieties. This work provides design principles and applications how to optimize MOFs for the preparation for versatile adsorbents using diamine grafting chemistry, which is also potentially applicable to various catalysis.

Critical differences in 3D atomic structure of individual ligand-protected nanocrystals in solution.

Precise three-dimensional (3D) atomic structure determination of individual nanocrystals is a prerequisite for understanding and predicting their physical properties. Nanocrystals from the same synthesis batch display what are often presumed to be small but possibly important differences in size, lattice distortions, and defects, which can only be understood by structural characterization with high spatial 3D resolution. We solved the structures of individual colloidal platinum nanocrystals by developing atomic-resolution 3D liquid-cell electron microscopy to reveal critical intrinsic heterogeneity of ligand-protected platinum nanocrystals in solution, including structural degeneracies, lattice parameter deviations, internal defects, and strain. These differences in structure lead to substantial contributions to free energies, consequential enough that they must be considered in any discussion of fundamental nanocrystal properties or applications.

First-principles database driven computational neural network approach to the discovery of active ternary nanocatalysts for oxygen reduction reaction.

An elegant machine-learning-based algorithm was applied to study the thermo-electrochemical properties of ternary nanocatalysts for oxygen reduction reaction (ORR). High-dimensional neural network potentials (NNPs) for the interactions among the components were parameterized from big dataset established by first-principles density functional theory calculations. The NNPs were then incorporated with Monte Carlo (MC) and molecular dynamics (MD) simulations to identify not only active, but also electrochemically stable nanocatalysts for ORR in acidic solution. The effects of surface strain caused by selective segregation of certain components on the catalytic performance were accurately characterized. The computationally efficient and precise approach proposes a promising ORR candidate: 2.6 nm icosahedron comprising 60% of Pt and 40% Ni/Cu. Our methodology can be applied for high-throughput screening and designing of key functional nanomaterials to drastically enhance the performance of various electrochemical systems.

First principles computational study on hydrolysis of hazardous chemicals phosphorus trichloride and oxychloride (PCl3 and POCl3) catalyzed by molecular water clusters.

Using first principles calculations we unveil fundamental mechanism of hydrolysis reactions of two hazardous chemicals PCl3 and POCl3 with explicit molecular water clusters nearby. It is found that the water molecules play a key role as a catalyst significantly lowing activation barrier of the hydrolysis via transferring its protons to reaction intermediates. Interestingly, torsional angle of the molecular complex at transition state is identified as a vital descriptor on the reaction rate. Analysis of charge distribution over the complex further reinforces the finding with atomic level correlation between the torsional angle and variation of the orbital hybridization state of phosphorus (P) in the complex. Electronic charge separation (or polarization) enhances thermodynamic stability of the activated complex and reduces the activation energy through hydrogen bonding network with water molecules nearby. Calculated potential energy surfaces (PES) for the hydrolysis of PCl3 and POCl3 depict their two contrastingly different profiles of double- and triple-depth wells, respectively. It is ascribed to the unique double-bonding O=P in the POCl3. Our results on the activation free energy show well agreements with previous experimental data within 7kcalmol-1 deviation.

First principles computational study on the adsorption mechanism of organic methyl iodide gas on triethylenediamine impregnated activated carbon.

We study removal of gas-phase organic methyl iodide (CH3I) from an ambient environment via adsorption onto triethylenediamine (TEDA) impregnated activated carbon (AC). First principles density functional theory (DFT) calculations and ab-initio molecular dynamics (AIMD) simulations were extensively utilized to understand the underlying mechanism for the chemical reaction of CH3I on the surface. Our results suggest that the adsorption energy of CH3I shows substantial heterogeneity depending on the adsorption site, porosity of the AC, and humidity. It is observed that the CH3I dissociative chemisorption is largely influenced by the adsorption site and porosity. Most importantly, it is clearly shown through free energy diagrams that the impregnated TEDA not only reduces the dissociation activation barrier of CH3I but also attracts H2O molecules relieving the AC surface from poisoning by humidity, and also enhances the removal efficiency of CH3I through the chemical dissociation reaction. Our computational study can help to open new routes to design highly efficient materials for removing environmentally and biologically hazardous materials, for example radioactive iodine gas emitted following accidents at a nuclear power plant.