Exchange Reactions between Alkanethiolates and Alkaneselenols on Au{111}

When alkanethiolate self-assembled monolayers on Au{111} are exchanged with alkaneselenols from solution, replacement of thiolates by selenols is rapid and complete, and is well described by perimeter-dependent island growth kinetics. The monolayer structures change as selenolate coverage increases, from being epitaxial and consistent with the initial thiolate structure to being characteristic of selenolate monolayer structures. At room temperature and at positive sample bias in scanning tunneling microscopy, the selenolate-gold attachment is labile, and molecules exchange positions with neighboring thiolates. The scanning tunneling microscope probe can be used to induce these place-exchange reactions

Applications of pattern recognition for dendritic microstructures

The Primary Dendrite Arm Spacing (PDAS) is the most important length scale in directionally solidified single crystal alloys. It determines the propensity for defect formation, solution heat treatment times and mechanical properties of the material. In this work a CMSX4 single crystal sample was imaged under a Scanning Electron Microscope (SEM). An automatic dendritic mapping (DenMap) algorithm using Normalised Cross-Correlation (NCC) is combined with Shape-Limited Primary Spacing (SLPS) to determine the local nearest neighbour dendrites and the corresponding dendritic packing. The algorithm located the dendritic centres, calculated the local PDAS, packing pattern, and relationship between PDAS and packing pattern for 256 dendrites in 1 minute 10 seconds. This is the first fully automatic method to produce a clear Gaussian distribution of local PDAS and packing pattern; thus, enabling rapid data gathering potential for single-crystal microstructures

An ultrahigh vacuum fast-scanning and variable temperature scanning tunneling microscope for large scale imaging

6 pags., 6 figs.We describe the design and performance of a fast-scanning, variable temperature scanning tunneling microscope (STM) operating from 80 to 700 K in ultrahigh vacuum (UHV), which routinely achieves large scale atomically resolved imaging of compact metallic surfaces. An efficient in-vacuum vibration isolation and cryogenic system allows for no external vibration isolation of the UHV chamber. The design of the sample holder and STM head permits imaging of the same nanometer-size area of the sample before and after sample preparation outside the STM base. Refractory metal samples are frequently annealed up to 2000 K and their cooldown time from room temperature to 80 K is 15 min. The vertical resolution of the instrument was found to be about 2 pm at room temperature. The coarse motor design allows both translation and rotation of the scanner tube. The total scanning area is about 8 × 8 μm2. The sample temperature can be adjusted by a few tens of degrees while scanning over the same sample area. © 2007 American Institute of Physics.This work was supported by the National Science Foundation under the Grant No. DMR-0134933 and by the Petroleum Research Fund No. 37999-

Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability

Data-driven algorithms for predicting mechanical properties with small datasets are evaluated in a case study on gear steel hardenability. The limitations of current data-driven algorithms and empirical models are identified. Challenges in analysing small datasets are discussed, and solution is proposed to handle small datasets with multiple variables. Gaussian methods in combination with novel predictive algorithms are utilized to overcome the challenges in analysing gear steel hardenability data and to gain insight into alloying elements interaction and structure homogeneity. The gained fundamental knowledge integrated with machine learning is shown to be superior to the empirical equations in predicting hardenability. Metallurgical-property relationships between chemistry, sample size, and hardness are predicted via two optimized machine learning algorithms: neural networks (NNs) and extreme gradient boosting (XGboost). A comparison is drawn between all algorithms, evaluating their performance based on small data sets. The results reveal that XGboost has the highest potential for predicting hardenability using small datasets with class imbalance and large inhomogeneity issues