Supercritical Water Gasification: Practical Design Strategies and Operational Challenges for Lab-Scale, Continuous Flow Reactors

Optimizing an industrial-scale supercritical water gasification process requires detailed knowledge of chemical reaction pathways, rates, and product yields. Laboratory-scale reactors are employed to develop this knowledge base. The rationale behind designs and component selection of continuous flow, laboratory-scale supercritical water gasification reactors is analyzed. Some design challenges have standard solutions, such as pressurization and preheating, but issues with solid precipitation and feedstock pretreatment still present open questions. Strategies for reactant mixing must be evaluated on a system-by-system basis, depending on feedstock and experimental goals, as mixing can affect product yields, char formation, and reaction pathways. In-situ Raman spectroscopic monitoring of reaction chemistry promises to further fundamental knowledge of gasification and decrease experimentation time. High-temperature, high-pressure spectroscopy in supercritical water conditions is performed, however, long-term operation flow cell operation is challenging. Comparison of Raman spectra for decomposition of formic acid in the supercritical region and cold section of the reactor demonstrates the difficulty in performing quantitative spectroscopy in the hot zone. Future designs and optimization of SCWG reactors should consider well-established solutions for pressurization, heating, and process monitoring, and effective strategies for mixing and solids handling for long-term reactor operation and data collection

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Federated Learning Enables Big Data for Rare Cancer Boundary Detection

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Design of a Small-Scale Mixing Section for a Supercritical Water Reactor through the Finite Volume Method

Thesis (Master's)--University of Washington, 2018Supercritical water (sc-H2O) reactors have been used for biomass gasification and the destruction of hazardous waste. Laboratory scale reactors are typically used for development of chemical kinetic rate parameters. These smaller reactors with lower Reynolds numbers often suffer from slow mixing between the reagent and sc-H2O; this slow mixing increases uncertainty in the data required for calculation of chemical kinetic rate parameters. In this study, we present a multiple-jet in crossflow design for a mixing section, which enables rapid mixing of reagents into sc-H2O. A parametric analysis is conducted to establish an optimum jet-to-crossflow velocity ratio (r) for scalar mixing using three-dimensional computational fluid dynamics (CFD) with Detached Eddy Simulations (DES) for resolving turbulence. Kinetic theory models for calculating physical properties of the fluids at the supercritical state are evaluated against data available in published literature. CFD simulations show that mixing can be characterized by three distinct regimes: (i) under-penetrating jets, (ii) weakly penetrating jets, (iii) jets forming counter-rotating vortex pairs (CVPs), and (iv) impinging jets. The best mixing is observed for jets forming CVPs; under-penetrating jets show the worst mixing. The mechanism of mixing in the three configurations is explained. Decomposition of methanol (MeOH) in a continuous-flow sc-H2O reactor is simulated with CFD using global first-order chemical kinetic rate parameters calculated from published experimental data. This numerical modeling sheds insight into the complex physiochemical processes of organic compound decomposition in the supercritical environment. The modeling approach can be used in industrial process optimization and to improve the design of new and existing systems

A Prospective Clinical Audit to Strengthen the Clinical Practices Affecting the Incidence of New-onset Atrial Fibrillation after Off-pump Coronary Artery Bypass Grafting

Introduction: New-onset Atrial Fibrillation (AF) carries significant morbidity and mortality risk for postoperative patients. Clinical practice guidelines aimed at preventing it are beneficial, with protocols in place to prevent deviations from the standard. Aim: To improve or strengthen the clinical practices that impact the incidence of new-onset AF after off-pump Coronary Artery Bypass Grafting (CABG). Materials and Methods: The present prospective clinical audit was conducted in the Department of Cardiac Anaesthesiology, Bhanubhai Madhuben Patel Cardiac Centre, Bhaikaka University, Anand, Gujarat, India, from January 2021 to June 2021. Study included 50 consecutive patients undergoing off-pump CABG surgery. The monitored standards included the continuation of beta-blocker therapy in the preoperative period, restarting them in the immediate postoperative period, and maintaining serum potassium (S.K+) within the range of 3.5-5.5 mEq/L. The incidence of AF was also noted. The data were analysed using Microsoft Excel. Results: The audit included a total of 50 patients, with 36 males with a mean age of 58.72 years, and 14 females with a mean age of 60.07 years. Preoperative beta-blocker/Calcium Channel Blocker (CCB) therapy on the day of surgery was administered to 45 (90%) patients, while restarting beta-blockers in the immediate postoperative period was done for 49 (98%) patients. S.K+ levels were maintained within the range in 31 (62%) patients. The last standard was reaudited, and compliance was achieved in 39 (78%) patients. New-onset AF occurred in 4 (8%) and 5 (10%) patients in the audit and reaudit samples, respectively. Conclusion: Clinical audit is a process that helps to identify the lacunae in clinical practices that affect patient outcomes. In the current study, clinical audits have aided in measuring compliance with different clinical practices, as per Institutional protocols. They have also assisted in increasing compliance with clinical practices where measured compliance was below the targeted goal

Factors affecting the oral health behavior of Indian married women: It’s lifestyle of something else? (a questionnaire based original research study)

With an objective to better understand differences in oral health related behavior and practices of working and non working, this study was conducted. Duration of the study was three months, from August 2021 to October 2021 and it was conducted in Department of Periodontics, Mansovar Dental College and Research Center, Bhopal (M.P.), India. A questionnaire based survey was conducted amongst 300 married female participants aged 18 years or more (111 working women and 189 non-working women). The samples were heterogeneous group from different occupation and economic status. None of the participants in the present study was found to have good oral hygiene and more than 50.0% of the patients were having poor oral hygiene. It is an observation which needs attention. It was hence concluded that the oral health related behavior of the working and non-working married woman differ significantly

Influence of acclimation temperature on the induction of heat-shock protein 70 in the catfish Horabagrus brachysoma (Gunther)

Every organism responds to heat stress by synthesizing a group of evolutionarily conserved proteins called the heat-shock proteins (HSPs) that, by acting as molecular chaperones, protect the cell against the aggregation of denatured proteins and play a significant role in adaptation to temperature. The present study aimed to investigate the critical thermal maxima (CTMax) and the expression of HSP70 in different tissues (gill, brain, muscle and liver) of an endemic catfish Horabagrus brachysoma acclimated at either 20 or 30A degrees C for 30 days. To understand the HSP70 response, fish acclimated to the two temperatures were exposed to preset temperatures (26, 30, 34, 36 and 38A degrees C for 20A degrees C acclimated fish and 32, 34, 36, 38 and 40A degrees C for 30A degrees C acclimated fish) for 2 h, followed by 1 h recovery at their respective acclimation temperatures. The HSP70 levels in the gill, brain, muscle and liver tissues were determined by Western blotting of one-dimensional sodium dodecyl sulphate-polyacrylamide gel electrophoresis. A significant (P < 0.05) increase in the CTMax values was observed for fish acclimated at 30A degrees C (41.86 +/- A 0.39A degrees C) than those acclimated at 20A degrees C (39.13 +/- A 0.18A degrees C). HSP70 was detected in all the tissues with the highest level in the liver followed by intermediate levels in muscle and brain, and lowest level in gill tissue, irrespective of the acclimation temperatures (20 or 30A degrees C). The HSP70 levels were significantly higher (P < 0.05) in the tissues of fish acclimated at 30A degrees C than those acclimated at 20A degrees C. The mean induction temperature of HSP70 in all the tissues of fish acclimated at either 20 or 30A degrees C was 30 and 34A degrees C, respectively. The optimum temperature for HSP70 induction in all the tissues of fish acclimated at 20A degrees C was 36A degrees C, whereas for fish acclimated at 30A degrees C was 36A degrees C for gill and 38A degrees C for brain, muscle and liver. Decreased levels of HSP70 were noted in all the tissues of fish when exposed to temperatures that exceeded the optimum temperatures for HSP70 inductions. Overall results indicated that acclimation temperature influences both temperature tolerance and induction of HSP70 in H. brachysoma