Multivariate Bayesian Optimization of CoO Nanoparticles for CO2 Hydrogenation Catalysis.

The hydrogenation of CO2 holds promise for transforming the production of renewable fuels and chemicals. However, the challenge lies in developing robust and selective catalysts for this process. Transition metal oxide catalysts, particularly cobalt oxide, have shown potential for CO2 hydrogenation, with performance heavily reliant on crystal phase and morphology. Achieving precise control over these catalyst attributes through colloidal nanoparticle synthesis could pave the way for catalyst and process advancement. Yet, navigating the complexities of colloidal nanoparticle syntheses, governed by numerous input variables, poses a significant challenge in systematically controlling resultant catalyst features. We present a multivariate Bayesian optimization, coupled with a data-driven classifier, to map the synthetic design space for colloidal CoO nanoparticles and simultaneously optimize them for multiple catalytically relevant features within a target crystalline phase. The optimized experimental conditions yielded small, phase-pure rock salt CoO nanoparticles of uniform size and shape. These optimized nanoparticles were then supported on SiO2 and assessed for thermocatalytic CO2 hydrogenation against larger, polydisperse CoO nanoparticles on SiO2 and a conventionally prepared catalyst. The optimized CoO/SiO2 catalyst consistently exhibited higher activity and CH4 selectivity (ca. 98%) across various pretreatment reduction temperatures as compared to the other catalysts. This remarkable performance was attributed to particle stability and consistent H* surface coverage, even after undergoing the highest temperature reduction, achieving a more stable catalytic species that resists sintering and carbon occlusion.

The Importance of the Morel Emotional Numbing Test Instructions: A Diagnosis Threat Induction Study.

OBJECTIVE: Marketed as a validity test that detects feigning of posttraumatic stress disorder (PTSD), the Morel Emotional Numbing Test for PTSD (MENT) instructs examinees that PTSD may negatively affect performance on the measure. This study explored the potential that MENT performance depends on inclusion of "PTSD" in its instructions and the nature of the MENT as a performance validity versus a symptom validity test (PVT/SVT). METHOD: 358 participants completed the MENT as a part of a clinical neuropsychological evaluation. Participants were either administered the MENT with the standard instructions (SIs) that referenced "PTSD" or revised instructions (RIs) that did not. Others were administered instructions that referenced "ADHD" rather than PTSD (AI). Comparisons were conducted on those who presented with concerns for potential traumatic-stress related symptoms (SI vs. RI-1) or attention deficit (AI vs. RI-2). RESULTS: Participants in either the SI or AI condition produced more MENT errors than those in their respective RI conditions. The relationship between MENT errors and other S/PVTs was significantly stronger in the SI: RI-1 comparison, such that errors correlated with self-reported trauma-related symptoms in the SI but not RI-1 condition. MENT failure also predicted PVT failure at nearly four times the rate of SVT failure. CONCLUSIONS: Findings suggest that the MENT relies on overt reference to PTSD in its instructions, which is linked to the growing body of literature on "diagnosis threat" effects. The MENT may be considered a measure of suggestibility. Ethical considerations are discussed, as are the construct(s) measured by PVTs versus SVTs.

Using Data-Driven Learning to Predict and Control the Outcomes of Inorganic Materials Synthesis.

The design of inorganic materials for various applications critically depends on our ability to manipulate their synthesis in a rational, robust, and controllable fashion. Different from the conventional trial-and-error approach, data-driven techniques such as the design of experiments (DoE) and machine learning are an effective and more efficient way to predictably control materials synthesis. Here, we present a Viewpoint on recent progress in leveraging such techniques for predicting and controlling the outcomes of inorganic materials synthesis. We first compare how the design choice (statistical DoE vs machine learning) affects the type of control it can offer over the resulting product attributes, information elucidated, and experimental cost. These attributes are supported by discussing select case studies from the recent literature that highlight the power of these techniques for materials synthesis. The influence of experimental bias is next discussed, followed finally by our perspectives on the major challenges in the widespread implementation of predictable and controllable materials synthesis using data-driven techniques.

Predictive Synthesis of Copper Selenides Using a Multidimensional Phase Map Constructed with a Data-Driven Classifier.

Copper selenides are an important family of materials with applications in catalysis, plasmonics, photovoltaics, and thermoelectrics. Despite being a binary material system, the Cu-Se phase diagram is complex and contains multiple crystal structures in addition to several metastable structures that are not found on the thermodynamic phase diagram. Consequently, the ability to synthetically navigate this complex phase space poses a significant challenge. We demonstrate that data-driven learning can successfully map this phase space in a minimal number of experiments. We combine soft chemistry (chimie douce) synthetic methods with multivariate analyses via classification techniques to enable predictive phase determination. A surrogate model was constructed with experimental data derived from a design matrix of four experimental variables: C-Se bond strength of the selenium precursor, time, temperature, and solvent composition. The reactions in the surrogate model resulted in 11 distinct phase combinations of copper selenide. These data were used to train a classification model that predicts the phase with 95.7% accuracy. The resulting decision tree enabled conclusions to be drawn about how the experimental variables affect the phase and provided prescriptive synthetic conditions for specific phase isolation. This guided the accelerated phase targeting in a minimum number of experiments of klockmannite CuSe, which could not be isolated in any of the reactions used to construct the surrogate model. The reaction conditions that the model predicted to synthesize klockmannite CuSe were experimentally validated, highlighting the utility of this approach.

Creating ground truth for nanocrystal morphology: a fully automated pipeline for unbiased transmission electron microscopy analysis.

Control over colloidal nanocrystal morphology (size, size distribution, and shape) is important for tailoring the functionality of individual nanocrystals and their ensemble behavior. Despite this, traditional methods to quantify nanocrystal morphology are laborious. New developments in automated morphology classification will accelerate these analyses but the assessment of machine learning models is limited by human accuracy for ground truth, causing even unsupervised machine learning models to have inherent bias. Herein, we introduce synthetic image rendering to solve the ground truth problem of nanocrystal morphology classification. By simulating 2D images of nanocrystal shapes via a function of high-dimensional parameter space, we trained a convolutional neural network to link unique morphologies to their simulated parameters, defining nanocrystal morphology quantitatively rather than qualitatively. An automated pipeline then processes, quantitatively defines, and classifies nanocrystal morphology from experimental transmission electron microscopy (TEM) images. Using improved computer vision techniques, 42 650 nanocrystals were identified, assessed, and labeled with quantitative parameters, offering a 600-fold improvement in efficiency over best-practice manual measurements. A classification algorithm was trained with a prediction accuracy of 99.5%, which can successfully analyze a range of concave, convex, and irregular nanocrystal shapes. The resulting pipeline was applied to differentiating two syntheses of nominally cuboidal CsPbBr3 nanocrystals and uniquely classifying binary nickel sulfide nanocrystal phase based on morphology. This pipeline provides a simple, efficient, and unbiased method to quantify nanocrystal morphology and represents a practical route to construct large datasets with an absolute ground truth for training unbiased morphology-based machine learning algorithms.

Hematoma After Continuous Erector Spinae Plane Block With Catheter Placement: A Case Report.

The erector spinae plane block (ESPB) is described as a safe and effective alternative when epidural or paravertebral blocks are contraindicated by anticoagulation therapy. We present a case of subcutaneous hematoma after ESPB catheter placement. The patient received bilateral ESPB catheters for perioperative pain control. Postoperatively, the patient developed tenderness to palpation at the left catheter site. Physical examination revealed a well circumscribed, fluctuant mass that produced bloody material during incision and drainage. This case report describes hematoma as a potential complication of the ESPB. After the procedure, patients should be closely monitored for complications, including hematoma.

Independent validation of downscaled climate estimates from a coastal Alaska watershed using local historical weather journals.

Downscaling coarse global and regional climate models allows researchers to access weather and climate data at finer temporal and spatial resolution, but there remains a need to compare these models with empirical data sources to assess model accuracy. Here, we validate a widely used software for generating North American downscaled climate data, ClimateNA, with a novel empirical data source, 20th century weather journals kept by Admiralty Island, Alaska homesteader, Allen Hasselborg. Using Hasselborg's journals, we calculated monthly precipitation and monthly mean of the maximum daily air temperature across the years 1926 to 1954 and compared these to ClimateNA data generated from the Hasselborg homestead location and adjacent areas. To demonstrate the utility and potential implications of this validation for other disciplines such as hydrology, we used an established regression equation to generate time series of 95% low duration flow estimates for the month of August using mean annual precipitation from ClimateNA predictions and Hasselborg data. Across 279 months, we found strong correlation between modeled and observed measurements of monthly precipitation (ρ = 0.74) and monthly mean of the maximum daily air temperature (ρ = 0.98). Monthly precipitation residuals (calculated as ClimateNA data - Hasselborg data) generally demonstrated heteroscedasticity around zero, but a negative trend in residual values starting during the last decade of observations may have been due to a shift to the cold-phase Pacific Decadal Oscillation. Air temperature residuals demonstrated a consistent but small positive bias, with ClimateNA tending to overestimate air temperature relative to Hasselborg's journals. The degree of correlation between weather patterns observed at the Hasselborg homestead site and ClimateNA data extracted from spatial grid cells across the region varied by wet and dry climate years. Monthly precipitation from both data sources tended to be more similar across a larger area during wet years (mean ρ across grid cells = 0.73) compared to dry years (mean ρ across grid cells = 0.65). The time series of annual 95% low duration flow estimates for the month of August generated using ClimateNA and Hasselborg data were moderately correlated (ρ = 0.55). Our analysis supports previous research in other regions which also found ClimateNA to be a robust source for past climate data estimates.

Statistical Multiobjective Optimization of Thiospinel CoNi2S4 Nanocrystal Synthesis via Design of Experiments.

Thiospinels, such as CoNi2S4, are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNi2S4 has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNi2S4 nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 25-2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/d̅) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.

Transesophageal Echocardiographic Evaluation of a Double Orifice Mitral Valve.

Intraoperative transesophageal echocardiography currently is used routinely for many cardiothoracic surgical procedures. Although it is often used for intraoperative cardiac monitoring and to confirm preoperative echocardiographic findings, it may sometimes result in the discovery of unexpected pathology. In this e-challenge, a patient was found to have a mitral valve abnormality that was not previously detected on the preoperative transthoracic echocardiogram. The mitral valve anomaly subsequently was evaluated to characterize the anatomy, interrogate the valve, and provide a diagnosis.