Monodispersed transition metal (Pt, Rh, Pd) nanoparticles (NP) in the 0.8–15 nm range have been synthesized and are being used to probe catalytic selectivity in multipath organic transformation reactions. For NP systems, the turnover rates and product distributions depend on their size, shape, oxidation states, and their composition in case of bimetallic NP systems. Dendrimer-supported platinum and rhodium NPs of less than 2 nm diameter usually have high oxidation states and can be utilized for catalytic cyclization and hydroformylation reactions which previously were produced only by homogeneous catalysis. Transition metal nanoparticles in metal core (Pt, Co)––inorganic shell (SiO2) structure exhibit exceptional thermal stability and are well-suited to perform catalytic reactions at high temperatures (>400 °C). Instruments developed in our laboratory permit the atomic and molecular level study of NPs under reaction conditions (SFG, ambient pressure XPS and high pressure STM). These studies indicate continuous restructuring of the metal substrate and the adsorbate molecules, changes of oxidation states with NP size and surface composition variations of bimetallic NPs with changes of reactant molecules. The facile rearrangement of NP catalysts required for catalytic turnover makes nanoparticle systems (heterogeneous, homogeneous and enzyme) excellent catalysts and provides opportunities to develop hybrid heterogeneous-homogeneous, heterogeneous-enzyme and homogeneous-enzyme catalyst systems.

Experimental discoveries followed by theoretical interpretations that pave the way of further advances by experimentalists is a developing pattern in modern surface chemistry and catalysis. The revolution of modern surface science started with the development of surface-sensitive techniques such as LEED, XPS, AES, ISS and SIMS, in which the close collaboration between experimentalists and theorists led to the quantitative determination of surface structure and composition. The experimental discovery of the chemical activity of surface defects and the trends in the reactivity of transitional metals followed by the explanations from the theoretical studies led to the molecular level understanding of active sites in catalysis. The molecular level knowledge, in turn, provided a guide for experiments to search for new generation of catalysts. These and many other examples of successes in experiment-and-theory-combined studies demonstrate the importance of the collaboration between experimentalists and theorists in the development of modern surface science.

Nickel-based catalysts play an important role in the chemical transformation of CO2. A fundamental understanding of the interaction between CO2 and Ni surfaces at atomic level is necessary. In this study, the interfacial reactions of CO2 and CO2 + H2O mixture on Ni(111) as well Ni(100) surfaces were investigated using ambient pressure X-ray photoelectron spectroscopy as well as theoretical calculations. The results indicate that the distributions of dissociation products are very different on the Ni(111) and Ni(100) surfaces. Carbonate, CO, and graphitic carbon formed on both Ni(111) and Ni(100) surfaces in the presence of 0.2 Torr CO2. However, more than 90% adsorption species on the Ni(111) surface is the carbonate, whereas the Ni(100) surface is mainly covered by adsorbed CO∗ and graphitic carbon. The co-adsorption of H2O weakens the influence of structure effect on the interfacial interaction between CO2 and Ni surfaces. Furthermore, the density functional theory calculations suggest that the activation of CO2 on Ni(111) and Ni(100) tends to follow different reaction paths, which are consistent with the experimental results.

Background

Impaired function of masticatory muscles will lead to trismus. Routine delineation of these muscles during planning may improve dose tracking and facilitate dose reduction resulting in decreased radiation-related trismus. This study aimed to compare a deep learning model with a commercial atlas-based model for fast auto-segmentation of the masticatory muscles on head and neck computed tomography (CT) images.

Material and methods

Paired masseter (M), temporalis (T), medial and lateral pterygoid (MP, LP) muscles were manually segmented on 56 CT images. CT images were randomly divided into training (n = 27) and validation (n = 29) cohorts. Two methods were used for automatic delineation of masticatory muscles (MMs): Deep learning auto-segmentation (DLAS) and atlas-based auto-segmentation (ABAS). The automatic algorithms were evaluated using Dice similarity coefficient (DSC), recall, precision, Hausdorff distance (HD), HD95, and mean surface distance (MSD). A consolidated score was calculated by normalizing the metrics against interobserver variability and averaging over all patients. Differences in dose (∆Dose) to MMs for DLAS and ABAS segmentations were assessed. A paired t-test was used to compare the geometric and dosimetric difference between DLAS and ABAS methods.

Results

DLAS outperformed ABAS in delineating all MMs (p < 0.05). The DLAS mean DSC for M, T, MP, and LP ranged from 0.83 ± 0.03 to 0.89 ± 0.02, the ABAS mean DSC ranged from 0.79 ± 0.05 to 0.85 ± 0.04. The mean value for recall, HD, HD95, MSD also improved with DLAS for auto-segmentation. Interobserver variation revealed the highest variability in DSC and MSD for both T and MP, and the highest scores were achieved for T by both automatic algorithms. With few exceptions, the mean ∆D98%, ∆D95%, ∆D50%, and ∆D2% for all structures were below 10% for DLAS and ABAS and had no detectable statistical difference (P > 0.05). DLAS based contours had dose endpoints more closely matched with that of the manually segmented when compared with ABAS.

Conclusions

DLAS auto-segmentation of masticatory muscles for the head and neck radiotherapy had improved segmentation accuracy compared with ABAS with no qualitative difference in dosimetric endpoints compared to manually segmented contours.

Recent advances of ambient pressure X-ray photoelectron spectroscopy (AP-XPS) have enabled the chemical composition and the electrical potential profile at a liquid/electrode interface under electrochemical reaction conditions to be directly probed. In this work, we apply this operando technique to study the surface chemical composition evolution on a Co metal electrode in 0.1 M KOH aqueous solution under various electrical biases. It is found that an ∼12.2 nm-thick layer of Co(OH)₂ forms at a potential of about -0.4 V_Ag/AgCl, and upon increasing the anodic potential to about +0.4 V_Ag/AgCl, this layer is partially oxidized into cobalt oxyhydroxide (CoOOH). A CoOOH/Co(OH)₂ mixture layer is formed on the top of the electrode surface. Finally, the oxidized surface layer can be reduced to Co⁰ at a cathodic potential of -1.35 V_Ag/Cl. These observations indicate that the ultrathin layer containing cobalt oxyhydroxide is the active phase for oxygen evolution reaction (OER) on a Co electrode in an alkaline electrolyte, consistent with previous studies.

In this work, ambient pressure X-ray photoelectron spectroscopy (APXPS) was used to investigate the effect of oxygen adsorption on the band bending and electron affinity of Al2O3, ZnO and TiO2 ultrathin films (∼1 nm in thickness) deposited on a Si substrate by atomic layer deposition (ALD). Upon exposure to oxygen at room temperature (RT), upward band bending was observed on all three samples, and a decrease in electron affinity was observed on Al2O3 and ZnO ultrathin films at RT. At 80 °C, the magnitude of the upward band bending decreased, and the change in the electron affinity vanished. These results indicate the existence of two surface oxygen species: a negatively charged species that is strongly adsorbed and responsible for the observed upward band bending, and a weakly adsorbed species that is polarized, lowering the electron affinity. Based on the extent of upward band bending on the three samples, the surface coverage of the strongly adsorbed species exhibits the following order: Al2O3 > ZnO > TiO2. This finding is in stark contrast to the trend expected on the surface of these bulk oxides, and highlights the unique surface activity of ultrathin oxide films with important implications, for example, in oxidation reactions taking place on these films or in catalyst systems where such oxides are used as a support material.

Purpose: To evaluate the accuracy of deep-learning-based auto-segmentation of the superior constrictor, middle constrictor, inferior constrictor, and larynx in comparison with a traditional multi-atlas-based method. Methods and Materials: One hundred and five computed tomography image datasets from 83 head and neck cancer patients were retrospectively collected and the superior constrictor, middle constrictor, inferior constrictor, and larynx were analyzed for deep-learning versus multi-atlas-based segmentation. Eighty-three computed tomography images (40 diagnostic computed tomography and 43 planning computed tomography) were used for training the convolutional neural network, and for atlas-based model training. The remaining 22 computed tomography datasets were used for validation of the atlas-based auto-segmentation versus deep-learning-based auto-segmentation contours, both of which were compared with the corresponding manual contours. Quantitative measures included Dice similarity coefficient, recall, precision, Hausdorff distance, 95th percentile of Hausdorff distance, and mean surface distance. Dosimetric differences between the auto-generated contours and manual contours were evaluated. Subjective evaluation was obtained from 3 clinical observers to blindly score the autosegmented structures based on the percentage of slices that require manual modification. Results: The deep-learning-based auto-segmentation versus atlas-based auto-segmentation results were compared for the superior constrictor, middle constrictor, inferior constrictor, and larynx. The mean Dice similarity coefficient values for the 4 structures were 0.67, 0.60, 0.65, and 0.84 for deep-learning-based auto-segmentation, whereas atlas-based auto-segmentation has Dice similarity coefficient results at 0.45, 0.36, 0.50, and 0.70, respectively. The mean 95th percentile of Hausdorff distance (cm) for the 4 structures were 0.41, 0.57, 0.59, and 0.54 for deep-learning-based auto-segmentation, but 0.78, 0.95, 0.96, and 1.23 for atlas-based auto-segmentation results, respectively. Similar mean dose differences were obtained from the 2 sets of autosegmented contours compared to manual contours. The dose-volume discrepancies and the average modification rates were higher with the atlas-based auto-segmentation contours. Conclusion: Swallowing-related structures are more accurately generated with DL-based versus atlas-based segmentation when compared with manual contours.

Though specific growth rate (SGR) has potential prognostic value for oropharyngeal squamous cell carcinoma (OPSCC), there is sparse literature defining these rates. Our aims were to establish the SGRs of primary tumors (PTs) and lymph nodes (LNs) in OPSCC and to correlate SGR with oncologic outcome. A pilot study was designed with a retrospective analysis examining 54 patients from the University of California, Davis with OPSCC (diagnosed 2012-2019). Radiation oncology software and pretreatment serial CT scans were used to measure PT and LN volumes to calculate SGR and doubling time (DT). The mean PT-SGR was 1.2 ± 2.2%/day and the mean LN-SGR was 1.6 ± 1.9%/day. There was no statistically significant difference between slow-growing and fast-growing cohorts in terms of age, gender, smoking status, tumor subsite, HPV status (as determined with p16 staining), initial volume, or overall stage. SGR had no impact on 2-year overall survival, disease-free survival, or disease-specific survival. We found the average daily growth rates for OPSCC to be 1.2%/day and 1.6%/day. Our findings suggest PT- and LN-SGR are independent factors, not heavily influenced by known biomarkers and patient characteristics, without a statistical impact on prognosis. This information has value in patient counseling regarding tumor growth and in providing patients worried about fast-growing tumors the appropriate reassurance.