A Comparative Study on the Selected Area Electron Diffraction Pattern of Fe Oxide/Au Core-shell Structured Nanoparticles

The selected area electron diffraction （SAED） pattern of magnetic iron oxide core/gold shell nanoparticles has been studied. For the composite particles with mean size less than 10 nm, their SAED pattern is found to be different from either the pattern of pure Fe oxide nanoparticles or that of pure Au particles. Based on the fact that the ring diameters of these composite particles fit the characteristic relation for the fcc structure, the Au atoms on surfaces of the concerned particles are supposed to pack in a way more tightly than they usually do in pure Au nanoparticles. The driving force for this is the coherency strain which enables the shell material at the heterostructured interface to adapt the lattice parameters of the core.

Determination of Space Group Pncm of Prehnite Using Electron Diffraction Method

Space symmetry of prehnite, which occurs in cavities and veins within Skarn from the Tieshan iron mineral deposit,Daye,Hubei province,Central China,has been determined using selected area electron diffraction (SAED) and convergent-beam electron diffraction (CBED) on the submicrometer scale.Our results confirm that the natural prehnite may have the structure with symmetry Pncm.The unit-cell parameters of investigated prehnite (a=0.458nm,b=0.555nm,and c=1.853nm) have been calculated by using the multicrystal diffraction rings of gold,the internal standard.

Automating selective area electron diffraction phase identification using machine learning

Selective area electron diffraction(SAED)patterns can provide valuable insight into the structure of a material.However,the manual identification of collected patterns can be a significant bottleneck in the overall phase classification workflow.In this work,we utilize the recent advances in computer vision and machine learning(ML)to automate the indexing of SAED patterns.The performance of six different ML algorithms is demonstrated using metallic plutonium-zirconium alloys.The most successful approach trained a neural network(NN)to make a classification of the phase and zone axis,and then utilized a second NN to synthesize multiple independent predictions of different tilts in a single sample to make an overall phase identification.The results demonstrate that automated SAED phase identification using ML is a viable route to accelerate materials characterization.

A New Orthorhombic Phase of ZrW_(1.6) Mo_(0.4)O_8 at Ambient Pressure

Nanocrystallite δ ′ ZrW 1.6 Mo 0.4 O 8 was prepared in the precursor route synthesis. The characterization by means of powder X ray diffraction (XRD) and selected area electron diffraction (SAED) showed it crystallized in a orthorhombic crystal system with a =0.896 9(7) nm, b =0.701 1(8) nm, c =0.596(1) nm. The possible space group is Pnnm (58) or Pnn2 (34). The compound crystallizes in a metastable phase during the synthesis process depending on temperature and crystallization time.

Effects of Multiple Strengthening Treatments on Mechanical Properties and Stability of Nanoscale Precipitated Phases in an Aluminum-Copper-Lithium Alloy

Effects of multiple strengthening treatments （i.e. ag mechanical properties and stability of nanoscale prec in this research. Various tests such as hardness, tensi ng either at three or four consecutive temperatures） on pitated phases in an AA2090 alloy have been evaluated e, electrical resistance, differential scanning calorimetric （DSC）, and transmission electron microscopy （TEM） have been performed. The results show that the ultimate tensile strength （UTS） and the yield strength of the samples aged at four consecutive temperatures （i.e. natural aging＋190℃＋150℃＋100℃） can be increased approximately to 660 and 610 MPa, respectively. It is also found that precipitation of T1 phase occurs during multiple aging process of the alloy and the higher amounts of enthalpies shown in DSC charts are linked to higher volume fraction of this precipitate. Furthermore, TEM observations reveal that T1 phase has plate shape morphology and its crystal structure is in the form of hcp with lattice parameters of a=0.467 nm and c=0.878 nm.

Statistical morphological identification of low-dimensional nanomaterials by using TEM

Nanomaterials with low-dimensional morphology display unique properties in catalysis and related fields,which are highly dependent on the structure and aspect ratio.Thus,accurate identification of the structure and morphology is the basis to correlate to the performance.However,the widely adopted techniques such as XRD is incapable to precise identify the aspect ratio of low-dimensional nanomaterials,not even to quantify the morphological uniformity with statistical deviation value.Herein,ZnO nanorod and nanosheet featured with one-and two-dimensional morphology were selected as model materials,which were prepared by the hydrothermal method and statistically characterized by transmission electron microscopy(TEM).The results indicate that ZnO nanorods and nanosheets display rod-like and orthohexagnal morphology,which mainly encapsulated with{100}and{001}planes,respectively.The 7.36±0.20 and 0.39±0.02 aspect ratio(c/a)of ZnO nanorods and nanosheets could be obtained through the integration of the(100)and(002)diffraction rings in selected area electron diffraction(SAED).TEM combining with the SAED is favorable compare with XRD,which not only provides more accurate aspect ratio results with standard deviation values but also requires very small amounts of sample.This work is supposed to provide a convenient and accurate method for the characterization of nanomaterials with low-dimensional morphology through TEM.