NO在赤铁矿(0001)表面吸附的第一性原理计算

氮氧化物NO_(x)(NO,NO_(2))是对人类健康有严重危害的大气污染物。近年来研究表明赤铁矿(α-Fe_(2)O_(3))可作为高效光催化剂去除大气中NO_(x),但NO_(x)气体在赤铁矿表面的吸附特性还未明确,阻碍了对其催化机理的进一步认识。基于密度泛函理论,采用包括电子强关联效应的PBE+U以及色散力修正的方法,对NO气体分子在α-Fe_(2)O_(3)(0001)晶面的吸附行为进行深入研究,发现基于PBE+U方法获得的吸附能(Ead=-0.64 eV)比PBE获得的Ead(-1.31 eV)低近50%。这是由于电子强关联项U的引入降低了表面铁原子d轨道对价带顶的贡献,抑制了其化学活性,而NO具有一个未成对的π*轨道电子,使得其对吸附基体的电子结构格外敏感。与+U不同,色散力修正不会显著改变体系的电子结构,只是使Ead略有增加(-0.18 eV)。采用统计力学的Langmuir公式计算NO在α-Fe_(2)O_(3)(0001)表面的热力学平衡占据数,发现基于+U的吸附能得到的平衡占据数与实验观测更为一致。这些结果揭示了电子强关联效应在α-Fe_(2)O_(3)表面化学中的重要作用,并为进一步研究NO_(x)在α-Fe_(2)O_(3)表面的光催化反应机理奠定了基础。

Viscoelasticity of amyloid plaques in transgenic mouse brain studied by Brillouin microspectroscopy and correlative Raman analysis

A myloidopathy is one of the most prominent hallmarks of Alkheimer's disease(AD),the leading cause of dementia worldwide,and is characterized by the accumulation of amyloid plaques in the brain parenchyma.The plaques consist of abnornal deposits mainly composed of an aggregation-prone protein fragment,B-amyloid 140/1-42,into the extracellular matrix.Brillouin micro-spectroscopy is an all-optical contactless technique that is based on the interaction between visible light and longitudinal acoustic waves or phonons,giving access to the viscoelasticity of a sample on a subcellular scale.Here,we describe the first application of micromechanical mapping based on Brillouin scattering spectroscopy to probe the stifness of individual amyloid plaques in the hippocampal part of the brain of a B-amyloid overexpressi ng transgenic mouse.Correlative analysis based on Brillouin and Raman microspectroscopy showed that amyloid plaques have a complex structure with a rigid core of B-pleated shoet conformation(B-aryloid)protein sur-rounded by a softer ring-shaped region richer in lipids and other protein conformations.These preliminary results give a new insight into the plaque biophysics and biomechanics,and a valuable contrast mechanism for the study and diagnosis of amnyloidopathy.

Optical and Electronic Properties of Monomers of Eumelanin: A DFT and TD-DFT Computational Study

We report a systematic investigation on the electronic and optical properties of four monomers which are elementary constituents of some of the protomolecules of eumelanin. Eumelanin is the most important form of melanin which is one of the most universal natural pigments in living organisms. For the isolated monomers we performed all-electrons Density Functional Theory (DFT) and Time Dependent DFT (TDDFT) calculations with a localized Gaussian basis-set. For each monomer we determined a series of molecular properties, namely electron affinities, ionization energies, fundamental energy-gaps, optical absorption spectra, and exciton binding energies. We discuss moreover the possible implications of the above electronic and optical properties of the single monomers with respect to the properties of a recently proposed tetrameric protomolecule of eumelanin.

Hydrodynamic finite-size scaling of the thermal conductivity in glasses

In the past few years,the theory of thermal transport in amorphous solids has been substantially extended beyond the Allen-Feldman model.The resulting formulation,based on the Green-Kubo linear response or the Wigner-transport equation,bridges this model for glasses with the traditional Boltzmann kinetic approach for crystals.The computational effort required by these methods usually scales as the cube of the number of atoms,thus severely limiting the size range of computationally affordable glass models.Leveraging hydrodynamic arguments,we show how this issue can be overcome through a simple formula to extrapolate a reliable estimate of the bulk thermal conductivity of glasses from finite models of moderate size.We showcase our findings for realistic models of paradigmatic glassy materials.

Grain boundary boosting the thermal stability of Pt/CeO_(2)thin films

Understanding how defect chemistry of oxide material influences the thermal stability of noble metal dopant ions plays an important role in designing high-performance heterogeneous catalytic systems.Here we use in-situ ambient-pressure X-ray photoemission spectroscopy(APXPS)to experimentally determine the role of grain boundary in the thermal stability of platinum doped cerium oxide(Pt/CeO_(2)).The grain boundaries were introduced in Pt/CeO_(2)thin films by pulsed laser deposition without significantly change of the surface microstructure.The defect level was tuned by the strain field obtained using a highly/low mismatched substrate.The Pt/CeO_(2)thin film models having well defined crystallographic properties but different grain boundary structural defect levels provide an ideal platform for exploring the evolution of Pt–O–Ce bond with changing the temperature in reducing conditions.We have direct demonstration and explanation of the role of Ce^(3+)induced by grain boundaries in enhancing Pt2+stability.We observe that the Pt^(2+)–O–Ce^(3+)bond provides an ideal coordinated site for anchoring of Pt^(2+)ions and limits the further formation of oxygen vacancies during the reduction with H_(2).Our findings demonstrate the importance of grain boundary in the atomic-scale design of thermally stable catalytic active sites.

Erratum to:Grain boundary boosting the thermal stability of Pt/CeO_(2)thin films

The name of the third author in original paper was unfortunately misspelled.It should be“Xiangchen Hu”,instead of“Xiangcheng Hu”.

Nano-confinement of biomolecules： Hydrophilic confinement promotes structural order and enhances mobility of water molecules

Molecular dynamics simulations have been used to investigate the confinement packing characteristics of small hydrophilic （N-acetyl-glycine-methylamide, Nagma） and hydrophobic （N-acetyl-leucine-methylamide, Nalma） biomolecules in large diameter single-wall carbon nanotubes （SWCNTs）. We find that hydrophilic biomolecules easily fill the nanotube and self organize into a geometrical configuration which reminds the water structural organization under SWCNT confinement. The packing of hydrophilic biomolecules inside the cylinder confines all water molecules in its core, which enhances their mobility. Conversely, hydrophobic biomolecules accommodate into the nanotubes with a trend for homogeneous filling, which generate unstable small pockets of water and drive toward a state of dehydration. These results shed light on key parameters important for the encapsulation of biomolecules with direct relevance for long-term storage and prevention of degradation.

Non-contact mechanical and chemical analysis of single living cells by microspectroscopic techniques

Innovative label-free microspectroscopy,which can simultaneously collect Brillouin and Raman signals,is used to characterize the viscoelastic properties and chemical composition of living cells with sub-micrometric resolution.The unprecedented statistical accuracy of the data combined with the high-frequency resolution and the high contrast of the recently built experimental setup permits the study of single living cells immersed in their buffer solution by contactless measurements.The Brillouin signal is deconvoluted in the buffer and the cell components,thereby revealing the mechanical heterogeneity inside the cell.In particular,a 20%increase is observed in the elastic modulus passing from the plasmatic membrane to the nucleus as distinguished by comparison with the Raman spectroscopic marker.Brillouin line shape analysis is even more relevant for the comparison of cells under physiological and pathological conditions.Following oncogene expression,cells show an overall reduction in the elastic modulus(15%)and apparent viscosity(50%).In a proof-of-principle experiment,the ability of this spectroscopic technique to characterize subcellular compartments and distinguish cell status was successfully tested.The results strongly support the future application of this technique for fundamental issues in the biomedical field.

A systematic approach to generating accurate neural network potentials:the case of carbon

Availability of affordable and widely applicable interatomic potentials is the key needed to unlock the riches of modern materials modeling.Artificial neural network-based approaches for generating potentials are promising;however,neural network training requires large amounts of data,sampled adequately from an often unknown potential energy surface.Here we propose a selfconsistent approach that is based on crystal structure prediction formalism and is guided by unsupervised data analysis,to construct an accurate,inexpensive,and transferable artificial neural network potential.Using this approach,we construct an interatomic potential for carbon and demonstrate its ability to reproduce first principles results on elastic and vibrational properties for diamond,graphite,and graphene,as well as energy ordering and structural properties of a wide range of crystalline and amorphous phases.

A DNA origami plasmonic sensor with environment-independent read-out

DNA origami is a promising technology for its reproducibility,flexibility,scalability and biocompatibility.Among the several potential applications,DNA origami has been proposed as a tool for drug delivery and as a contrast agent,since a conformational change upon specific target interaction may be used to release a drug or produce a physical signal,respectively.However,its conformation should be robust with respect to the properties of the medium in which either the recognition or the read-out take place,such as pressure,viscosity and any other unspecific interaction other than the desired target recognition.Here we report on the read-out robustness of a tetragonal DNA-origami/gold-nanoparticle hybrid structure able to change its configuration,which is transduced in a change of its plasmonic properties,upon interaction with a specific DNA target.We investigated its response when analyzed in three different media:aqueous solution,solid support and viscous gel.We show that,once a conformational variation is produced,it remains unaffected by the subsequent physical interactions with the environment.

Inclusion of infrared dielectric screening in the GW method from polaron energies to charge mobilities

We introduce in the many-body GW scheme the modulation of the screened Coulomb interaction W arising from the macroscopic dielectric response in the infrared.We derive expressions for the polaron binding energies,the renormalization of the effective masses and for the electron and hole relaxation times.Electron and hole mobilities are then obtained from the incorporation of appropriate scattering rules.Zinc-blende GaN and orthorhombic MAPbI3 are used as test beds finding fair agreement with results from rigorous electron-phonon coupling approaches.Although limited to polar phonons,our method has a negligible computational cost.

Dynamical properties of water in living cells

With the aim of studying the effect of water dynamics on the properties of biological systems, in this paper, we present a quasi-elastic neutron scattering study on three different types of living cells, differing both in their morphological and tumor properties. The measured scattering signal, which essentially originates from hydrogen atoms present in the investigated systems, has been analyzed using a global fitting strategy using an optimized theoretical model that considers various classes of hydrogen atoms and allows disentangling diffusive and rotational motions. The approach has been carefully validated by checking the reliability of the calculation of parameters and their 99% confidence intervals. We demonstrate that quasi-elastic neutron scattering is a suitable experimental technique to characterize the dynamics of intracellular water in the angstrom/picosecond space/time scale and to investigate the effect of water dynamics on cellular biodiversity.

Deep Learning,Feature Learning,and Clustering Analysis for SEM Image Classification

In this paper,we report upon our recent work aimed at improving and adapting machine learning algorithms to automatically classify nanoscience images acquired by the Scanning Electron Microscope(SEM).This is done by coupling supervised and unsupervised learning approaches.We first investigate supervised learning on a ten-category data set of images and compare the performance of the different models in terms of training accuracy.Then,we reduce the dimensionality of the features through autoencoders to perform unsupervised learning on a subset of images in a selected range of scales(from 1μm to 2μm).Finally,we compare different clustering methods to uncover intrinsic structures in the images.