Reaction mechanism and kinetics for CO₂ reduction on nickel single atom catalysts from quantum mechanics

Experiments have shown that graphene-supported Ni-single atom catalysts (Ni-SACs) provide a promising strategy for the electrochemical reduction of CO₂ to CO, but the nature of the Ni sites (Ni-N₂C₂, Ni-N₃C₁, Ni-N₄) in Ni-SACs has not been determined experimentally. Here, we apply the recently developed grand canonical potential kinetics (GCP-K) formulation of quantum mechanics to predict the kinetics as a function of applied potential (U) to determine faradic efficiency, turn over frequency, and Tafel slope for CO and H₂ production for all three sites. We predict an onset potential (at 10 mA cm⁻²) U_(onset) = −0.84 V (vs. RHE) for Ni-N₂C₂ site and U_(onset) = −0.92 V for Ni-N₃C₁ site in agreement with experiments, and U_(onset) = −1.03 V for Ni-N₄. We predict that the highest current is for Ni-N₄, leading to 700 mA cm⁻² at U = −1.12 V. To help determine the actual sites in the experiments, we predict the XPS binding energy shift and CO vibrational frequency for each site

Rolling nanoelectrode lithography

Non-uniformity and low throughput issues severely limit the application of nanoelectrode lithography for large area nanopatterning. This paper proposes, for the first time, a new rolling nanoelectrode lithography approach to overcome these challenges. A test-bed was developed to realize uniform pressure distribution over the whole contact area between the roller and the silicon specimen, so that the local oxidation process occurred uniformly over a large area of the specimen. In this work, a brass roller wrapped with a fabricated polycarbonate strip was used as a stamp to generate nanopatterns on a silicon surface. The experimental results show that a uniform pattern transfer for a large area can be achieved with this new rolling nanoelectrode lithography approach. The rolling speed and the applied bias voltage were identified as the primary control parameters for oxide growth. Furthermore, the pattern direction showed no significant influence on the oxide process. We therefore demonstrated that nanoelectrode lithography can be scaled up for large-area nanofabrication by incorporating a roller stamp

Substrate orientation effects on nanoelectrode lithography : ReaxFF molecular dynamics and experimental study

The crystallographic orientation of the substrate is an essential parameter in the kinetic mechanism for the oxidation process. Hence, the choice of substrate surface orientation is crucial in nanofabrication industries. In the present work, we have studied qualitatively the influence of substrate orientation in nanoelectrode lithography using ReaxFF reactive molecular dynamics simulation. We have investigated the oxidation processes on (100), (110) and (111) orientation surfaces of silicon at different electric field intensities. The simulation results show the thickness of the oxide film and the initial oxygen diffusion rate follow an order of (100) > (110) > (111) at lower electric field intensities. It also confirms that surfaces with higher surface energy are more reactive at lower electric field intensity. Crossovers occurred at a higher electric field intensity (7 V nm -1) under which the thickness of the oxide film yields an order of T(110) > T(100) > T(111). These types of anomalous characteristics have previously been observed for thermal oxidation of silicon surfaces. Experimental results show different orders for the (100) and (111) substrate, while (110) remains the largest for the oxide thickness. A good correlation has been found between the oxide growth and the orientation-dependent parameters where the oxide growth is proportional to the areal density of the surfaces. The oxide growth also follows the relative order of the activation energies, which could be another controlling factor for the oxide growth. Less activation energy of the surface allows more oxide growth and vice versa. However, the differences between simulation and experimental results probably relate to the empirical potential as well as different time and spatial scales of the process

Underwater Image Super-Resolution using Deep Residual Multipliers

We present a deep residual network-based generative model for single image super-resolution (SISR) of underwater imagery for use by autonomous underwater robots. We also provide an adversarial training pipeline for learning SISR from paired data. In order to supervise the training, we formulate an objective function that evaluates the \textit{perceptual quality} of an image based on its global content, color, and local style information. Additionally, we present USR-248, a large-scale dataset of three sets of underwater images of 'high' (640x480) and 'low' (80x60, 160x120, and 320x240) spatial resolution. USR-248 contains paired instances for supervised training of 2x, 4x, or 8x SISR models. Furthermore, we validate the effectiveness of our proposed model through qualitative and quantitative experiments and compare the results with several state-of-the-art models' performances. We also analyze its practical feasibility for applications such as scene understanding and attention modeling in noisy visual conditions

Representation Learning for Sequential Volumetric Design Tasks

Volumetric design, also called massing design, is the first and critical step in professional building design which is sequential in nature. As the volumetric design process is complex, the underlying sequential design process encodes valuable information for designers. Many efforts have been made to automatically generate reasonable volumetric designs, but the quality of the generated design solutions varies, and evaluating a design solution requires either a prohibitively comprehensive set of metrics or expensive human expertise. While previous approaches focused on learning only the final design instead of sequential design tasks, we propose to encode the design knowledge from a collection of expert or high-performing design sequences and extract useful representations using transformer-based models. Later we propose to utilize the learned representations for crucial downstream applications such as design preference evaluation and procedural design generation. We develop the preference model by estimating the density of the learned representations whereas we train an autoregressive transformer model for sequential design generation. We demonstrate our ideas by leveraging a novel dataset of thousands of sequential volumetric designs. Our preference model can compare two arbitrarily given design sequences and is almost 90% accurate in evaluation against random design sequences. Our autoregressive model is also capable of autocompleting a volumetric design sequence from a partial design sequence

Improved Machine Learning-Based Predictive Models for Breast Cancer Diagnosis

Breast cancer death rates are higher than any other cancer in American women. Machine learning-based predictive models promise earlier detection techniques for breast cancer diagnosis. However, making an evaluation for models that efficiently diagnose cancer is still challenging. In this work, we proposed data exploratory techniques (DET) and developed four different predictive models to improve breast cancer diagnostic accuracy. Prior to models, four-layered essential DET, e.g., feature distribution, correlation, elimination, and hyperparameter optimization, were deep-dived to identify the robust feature classification into malignant and benign classes. These proposed techniques and classifiers were implemented on the Wisconsin Diagnostic Breast Cancer (WDBC) and Breast Cancer Coimbra Dataset (BCCD) datasets. Standard performance metrics, including confusion matrices and K-fold cross-validation techniques, were applied to assess each classifier’s efficiency and training time. The models’ diagnostic capability improved with our DET, i.e., polynomial SVM gained 99.3%, LR with 98.06%, KNN acquired 97.35%, and EC achieved 97.61% accuracy with the WDBC dataset. We also compared our significant results with previous studies in terms of accuracy. The implementation procedure and findings can guide physicians to adopt an effective model for a practical understanding and prognosis of breast cancer tumors.publishedVersio

Risk network of global energy markets

This study evaluates extreme uncertainty connectedness among top global energy firms. The sample comprises of 68 firms from four energy-related subsectors (oil & gas, oil & gas related equipment and services, multiline utilities, and renewable energy). To provide an overview of tail connectedness, we construct a high-dimensional network between firms by utilizing a generalized error decomposition and a sparse vector autoregression framework with a latent common factor. Our empirical results indicate that between the four subsectors, the renewable energy subsector exhibits the highest uncertainty transmission to other underlying subsectors, primarily credited to an increased within-subsector idiosyncratic uncertainty before the COVID-19 crisis. After the burst of the COVID-19 pandemic, due to the higher connectedness, the role of the renewable energy companies in the spillover network is further intensified. The uncertainty connectedness demonstrates a time-varying trait. While the oil and gas subsector exhibits greater long-term linkages with the oil and gas related equipment and services subsector, the long-run dynamics exhibit a lower interconnectedness as compared to the short-run. Finally, there is an increased connectedness among companies operating in the same subsector with similar size, attributing to similarity and competition

Promising lithography techniques for next generation logic devices : a review

Continuous rapid shrinking of feature size made the authorities to seek alternative patterning methods as the conventional photolithography comes with its intrinsic resolution limit. In this regard, some promising techniques have been proposed as next generation lithography (NGL) that have the potentials to achieve both high volume production and very high resolution. This article reviews the promising next generation lithography techniques and introduces the challenges and a perspective on future directions of the NGL techniques. Extreme Ultraviolet Lithography (EUVL) is considered as the main candidate for sub-10 nm manufacturing and it could potentially meet the current requirements of the industry. Remarkable progress in EUVL has been made and the tools will be available for commercial operation soon. Maskless lithography techniques are used for patterning in R&D, mask/mold fabrication and low volume chip design. Directed Self Assembly (DSA) has already been realized in laboratory and further effort will be needed to make it as NGL solution. Nanoimprint Lithography has emerged attractively due to its simple process-steps, high-throughput, high-resolution and low-cost and become one of the commercial platforms for nanofabrication. However, a number of challenging issues are waiting ahead and further technological progresses are required to make the techniques significant and reliable to meet the current demand. Finally, a comparative study is presented among these techniques

Ultrasound-Guided Attenuation Parameter May Replace B-mode Ultrasound in Diagnosing Nonalcoholic Fatty Liver Disease

Objective: To compare the diagnostic sensitivity and consistency of ultrasound-guided attenuation parameter (UGAP) with B-mode ultrasound in nonalcoholic fatty liver disease (NAFLD) patients, and explored their correlation with clinical indicators. Methods: Patients suspected of NAFLD from July to November 2021 were enrolled in this prospective study. After performing the B-mode ultrasound and UGAP examination, all patients were divided into four groups according to the grade of NAFLD obtained by two modalities, respectively. The diagnostic agreement of the two modalities were evaluated, and the diagnostic sensitivity was compared by the McNemar test. The correlation between clinical indicators and the attenuation coefficient (AC) of UGAP was analyzed by linear regression. Results: The intraclass correlation coefficient of UGAP was 0.958 (95%CI: 0.943,0.970), while the kappa value of B-mode ultrasound grading was 0.799 (95%CI: 0.686, 0.912). The diagnostic sensitivity of UGAP was higher than that of B-mode ultrasound (99.0% vs. 32%, P < 0.001). BMI and TG can be distinguished in different grades of NAFLD diagnosed by B-mode ultrasound, while BMI, ALT, HDL, and Apo A can be distinguished in different grades of NAFLD diagnosed by UGAP. BMI (r = 0.502, P < 0.001), ALT (r = 0. 396, P < 0.001), TG (r = 0.418, P < 0.001), HDL (r = -0. 359, P < 0.001) and Apo A (r = -0.228, P = 0.020) were linearly correlated with the AC value of UGAP. Conclusions: Compared with the B-mode ultrasound, UGAP had a higher sensitivity and consistency in diagnosing NAFLD, and correlated well with some laboratory indicators, which may be more valuable in screening and diagnosis of NAFLD