Effects of Oxygen Pressure on the Microstructures and Nanomechanical Properties of Samarium-Doped BiFeO3 Thin Films.

In this study, samarium (Sm-10at%)-doped BiFeO3 (SmBFO) thin films were grown on platinum-coated glass substrates using pulsed laser deposition (PLD) to unveil the correlation between the microstructures and nanomechanical properties of the films. The PLD-derived SmBFO thin films were prepared under various oxygen partial pressures (PO2) of 10, 30, and 50 mTorr at a substrate temperature of 600 °C. The scanning electron microscopy analyses revealed a surface morphology consisting of densely packed grains, although the size distribution varied with the PO2. X-ray diffraction results indicate that all SmBFO thin films are textured and preferentially oriented along the (110) crystallographic orientation. The crystallite sizes of the obtained SmBFO thin films calculated from the Scherrer and (Williamson-Hall) equations increased from 20 (33) nm to 25 (52) nm with increasing PO2. In addition, the nanomechanical properties (the hardness and Young's modulus) of the SmBFO thin films were measured by using nanoindentation. The relationship between the hardness and crystalline size of SmBFO thin films appears to closely follow the Hall-Petch equation. In addition, the PO2 dependence of the film microstructure, the crystallite size, the hardness, and Young's modulus of SmBFO thin films are discussed.

Comparative analysis of machine learning approaches to classify tumor mutation burden in lung adenocarcinoma using histopathology images.

Both histologic subtypes and tumor mutation burden (TMB) represent important biomarkers in lung cancer, with implications for patient prognosis and treatment decisions. Typically, TMB is evaluated by comprehensive genomic profiling but this requires use of finite tissue specimens and costly, time-consuming laboratory processes. Histologic subtype classification represents an established component of lung adenocarcinoma histopathology, but can be challenging and is associated with substantial inter-pathologist variability. Here we developed a deep learning system to both classify histologic patterns in lung adenocarcinoma and predict TMB status using de-identified Hematoxylin and Eosin (H&E) stained whole slide images. We first trained a convolutional neural network to map histologic features across whole slide images of lung cancer resection specimens. On evaluation using an external data source, this model achieved patch-level area under the receiver operating characteristic curve (AUC) of 0.78-0.98 across nine histologic features. We then integrated the output of this model with clinico-demographic data to develop an interpretable model for TMB classification. The resulting end-to-end system was evaluated on 172 held out cases from TCGA, achieving an AUC of 0.71 (95% CI 0.63-0.80). The benefit of using histologic features in predicting TMB is highlighted by the significant improvement this approach offers over using the clinical features alone (AUC of 0.63 [95% CI 0.53-0.72], p = 0.002). Furthermore, we found that our histologic subtype-based approach achieved performance similar to that of a weakly supervised approach (AUC of 0.72 [95% CI 0.64-0.80]). Together these results underscore that incorporating histologic patterns in biomarker prediction for lung cancer provides informative signals, and that interpretable approaches utilizing these patterns perform comparably with less interpretable, weakly supervised approaches.

Formation and Application of Core-Shell of FePt-Au Magnetic-Plasmonic Nanoparticles.

Monodispersed FePt core and FePt-Au core-shell nanoparticles (NPs) have been chemically synthesized in liquid solution and with controllable surface-functional properties. The NP size was increased from 2.5 nm for FePt to 6.5 nm for FePt-Au, which could be tuned by the initial concentration of gold acetate coated onto FePt seeding NPs via a seed-mediated formation of self-assembled core-shell nanostructures. The analyses of the interplanar spacing obtained from the high-resolution transmission electron microscopy (HRTEM), selective electron diffraction pattern (SAED), and x-ray diffraction (XRD) confirmed that both FePt core and Au shell belong to the face-centered cubic (fcc) structure. FePt-Au NPs have a surface plasmon resonance (SPR) peak at 528 nm in the visible optical band region, indicating the red shift compared with the typical theoretical value of 520 nm of pure Au NPs. The surface modification and ligand exchange of FePt-Au was using mercaptoacetic acid (thiol) as a phase transfer reagent that turned the NPs hydrophilic due to the functional carboxyl group bond on the surface of presented multifunctional magnetic-plasmonic NPs. The water-dispersible FePt-based NPs conjugated with biomolecules could reach the different biocompatibility requirements and also provide enough heating response that acted as a potential agent for magnetic fluid hyperthermia in biomedical engineering research fields.

Influence of Post-Annealing on the Structural and Nanomechanical Properties of Co Thin Films.

The correlations between the microstructure and nanomechanical properties of a series of thermal annealed Co thin films were investigated. The Co thin films were deposited on glass substrates using a magnetron sputtering system at ambient conditions followed by subsequent annealing conducted at various temperatures ranging from 300 C to 800 C. The XRD results indicated that for annealing temperature in the ranged from 300 C to 500 C, the Co thin films were of single hexagonal close-packed (hcp) phase. Nevertheless, the coexistence of hcp-Co (002) and face-centered cubic (fcc-Co (111)) phases was evidently observed for films annealed at 600 C. Further increasing the annealing temperature to 700 C and 800 C, the films evidently turned into fcc-Co (111). Moreover, significant variations in the hardness and Young's modulus are observed by continuous stiffness nanoindentation measurement for films annealed at different temperatures. The correlations between structures and properties are discussed.

Structural and nanomechanical properties of BiFeO3 thin films deposited by radio frequency magnetron sputtering.

The nanomechanical properties of BiFeO3 (BFO) thin films are subjected to nanoindentation evaluation. BFO thin films are grown on the Pt/Ti/SiO2/Si substrates by using radio frequency magnetron sputtering with various deposition temperatures. The structure was analyzed by X-ray diffraction, and the results confirmed the presence of BFO phases. Atomic force microscopy revealed that the average film surface roughness increased with increasing of the deposition temperature. A Berkovich nanoindenter operated with the continuous contact stiffness measurement option indicated that the hardness decreases from 10.6 to 6.8 GPa for films deposited at 350°C and 450°C, respectively. In contrast, Young's modulus for the former is 170.8 GPa as compared to a value of 131.4 GPa for the latter. The relationship between the hardness and film grain size appears to follow closely with the Hall-Petch equation.

Persistent topology and metastable state in conformational dynamics.

The large amount of molecular dynamics simulation data produced by modern computational models brings big opportunities and challenges to researchers. Clustering algorithms play an important role in understanding biomolecular kinetics from the simulation data, especially under the Markov state model framework. However, the ruggedness of the free energy landscape in a biomolecular system makes common clustering algorithms very sensitive to perturbations of the data. Here, we introduce a data-exploratory tool which provides an overview of the clustering structure under different parameters. The proposed Multi-Persistent Clustering analysis combines insights from recent studies on the dynamics of systems with dominant metastable states with the concept of multi-dimensional persistence in computational topology. We propose to explore the clustering structure of the data based on its persistence on scale and density. The analysis provides a systematic way to discover clusters that are robust to perturbations of the data. The dominant states of the system can be chosen with confidence. For the clusters on the borderline, the user can choose to do more simulation or make a decision based on their structural characteristics. Furthermore, our multi-resolution analysis gives users information about the relative potential of the clusters and their hierarchical relationship. The effectiveness of the proposed method is illustrated in three biomolecules: alanine dipeptide, Villin headpiece, and the FiP35 WW domain.

Building Markov state models with solvent dynamics.

BACKGROUND: Markov state models have been widely used to study conformational changes of biological macromolecules. These models are built from short timescale simulations and then propagated to extract long timescale dynamics. However, the solvent information in molecular simulations are often ignored in current methods, because of the large number of solvent molecules in a system and the indistinguishability of solvent molecules upon their exchange. METHODS: We present a solvent signature that compactly summarizes the solvent distribution in the high-dimensional data, and then define a distance metric between different configurations using this signature. We next incorporate the solvent information into the construction of Markov state models and present a fast geometric clustering algorithm which combines both the solute-based and solvent-based distances. RESULTS: We have tested our method on several different molecular dynamical systems, including alanine dipeptide, carbon nanotube, and benzene rings. With the new solvent-based signatures, we are able to identify different solvent distributions near the solute. Furthermore, when the solute has a concave shape, we can also capture the water number inside the solute structure. Finally we have compared the performances of different Markov state models. The experiment results show that our approach improves the existing methods both in the computational running time and the metastability. CONCLUSIONS: In this paper we have initiated an study to build Markov state models for molecular dynamical systems with solvent degrees of freedom. The methods we described should also be broadly applicable to a wide range of biomolecular simulation analyses.

Sputter-prepared (001) BiFeO3 thin films with ferromagnetic L10-FePt(001) electrode on glass substrates.

Highly textured BiFeO3(001) films were formed on L10-FePt(001) bottom electrodes on glass substrates by sputtering at reduced temperature of 400°C. Good electric polarization 2Pr = 80 and 95 µC/cm2, comparable to that of the reported epitaxial films, and coercivity Ec = 415 and 435 kV/cm are achieved in the samples with 20-nm- and 30-nm-thick electrodes. The BiFeO3(001) films show different degrees of compressive strain. The relation between the variations of strain and 2Pr suggests that the enhancement of 2Pr resulted from the strain-induced rotation of spontaneous polarization. The presented results open possibilities for the applications based on electric-magnetic interactions.