Reduction Rate Research Articles

Realizing the Efficient Dissolution of Drag Reducer upon pH-Induced Demulsification of Inverse Polymer Emulsion.

The increasing exploitation of fossil resources needs to shift people's attention to developing unconventional reservoir resources. Polyacrylamide-based emulsion drag reducers can effectively reduce the turbulence of fracturing fluid and improve oil recovery, but their release effect is poor, which limits their practical application. Here, we constructed a pH-responsive inverse polymer emulsion of poly(acrylamide-2-acrylamido-2-methylpropanesulfonic acid). Interestingly, HOA/Tween 80 exhibits remarkable pH-responsive behavior, which enables the monomer emulsion to change the type of emulsion under pH stimulation. Our results propose that the P(AM-AMPS) polymer emulsion is eluted with the same aqueous phase as the W/O P(AM-AMPS) polymer emulsion, thereby achieving the effect of drag reduction. P(AM-AMPS) can be rapidly released from the inverse polymer emulsion within 70 s upon pH stimulation, the drag reduction rate of which was 60.4%. Obviously, the inverse polymer emulsion prepared by a pH-responsive surfactant not only has good stability but also can achieve rapid release of the polymer upon pH stimulation, which supplies an interesting indication to explain why a balance exists between the stability of the emulsion and release of P(AM-AMPS).

Smelting Reduction of HIsarna Slag with Methane

To study the smelting reduction behavior of methane in the HIsarna process, the isothermal reduction experiments of HIsarna slag with 6 wt% of FeO were conducted at the temperatures of 1450, 1475, and 1550 °C, using 5 vol% CH4‐Ar. The effect of the gas injection flow rate on the overall reduction rate was investigated in the range of 0.5–1.5 L min−1. The results confirm that the decomposed carbon black acts as the dominant reductant in the reduction process compared to hydrogen. Increased gas flow rate increases the reaction rate, while temperature shows no significant effect. The reduction process could be described by the JMAEK model: by fixing the gas flow rate at 1.0 L min−1 with different temperatures, the exponent n value is around 1.5, indicating FeO mass transfer is the limiting step since there are sufficient carbon blacks as nucleation sites. The reduction process follows the first‐order model controlled by the reactant concentration with the gas flow rate being at 0.5 L min−1 and in the second reaction stage of the gas flow rate at 1.5 L min−1. The determined activation energy for the methane reduction is 59.8 kJ mol−1.

Niraparib Population Pharmacokinetics and Exposure-Response Relationships in Patients With Newly Diagnosed Advanced Ovarian Cancer

PurposeNiraparib is a poly(adenosine diphosphate [ADP]-ribose) polymerase inhibitor approved for the maintenance treatment of advanced ovarian cancer (OC). Niraparib was originally approved in recurrent OC at a fixed starting dose (FSD) of 300 mg once daily (QD). This analysis characterized the population pharmacokinetics (PK) of niraparib and evaluated the relationships between exposure, efficacy, and safety to support clinical use of an individualized dosing strategy, in which the starting dose of niraparib was adjusted based on patient characteristics to improve the benefit-risk profile. MethodsA population PK model was developed by pooling data from four niraparib clinical trials (PN001 [n = 104], QUADRA [n = 455], NOVA [n = 403], and PRIMA [n = 480]) in patients with solid tumors, including OC. Exposure-response analyses were conducted to explore the relationships of niraparib exposure with progression-free survival (PFS) and adverse events in the PRIMA study. A multivariate logistic regression model was also developed to estimate the probability of grade ≥3 thrombocytopenia, using data from patients enrolled in PRIMA and NOVA. The impact of an individualized starting dose (ISD) regimen (200 mg QD in patients with body weight [BW] <77 kg or platelet count [PLT] <150,000/µL, or 300 mg QD in patients with BW ≥77 kg and PLT ≥150,000/µL) on systemic exposure, efficacy, and safety was assessed. FindingsNiraparib disposition was best described by a 3-compartment model with linear elimination. Key covariates included baseline creatinine clearance, BW, albumin, and age, all of which had minor effects on niraparib exposure. Comparable model-predicted exposure up to the time of disease progression/death or censoring in the 300-mg FSD and 200-/300-mg ISD groups was consistent with the lower rate of dose reduction in the ISD groups. No consistent niraparib exposure-response relationship was observed for efficacy in all PRIMA patients (first-line OC), and no statistically significant difference was seen in PFS curves for patients receiving a niraparib dose of 200 mg versus 300 mg. In the multivariate regression model, performed using combined data from PRIMA and NOVA, higher niraparib exposure (area under the concentration-time curve at steady-state [AUCss]), lower BW, and lower PLT were associated with an increased risk of grade ≥3 thrombocytopenia. ImplicationsPopulation PK and exposure-response analyses support use of an ISD to improve the safety profile of niraparib, including reducing the rate of grade ≥3 thrombocytopenia, without compromising efficacy. Clinical trial registrationClinicalTrials.gov, NCT01847274 (NOVA), NCT00749502 (PN001), NCT02655016 (PRIMA), NCT02354586 (QUADRA), www.clinicaltrials.gov.

Study on electrochemical polishing mechanism of TC4 titanium alloy

Abstract To achieve a high-quality and precise surface on TC4 titanium alloy for demanding engineering applications, electrochemical polishing (ECP) technology was employed to investigate the polishing process of TC4 titanium alloy in an ethylene glycol-sodium chloride green electrolyte. The macro and micro morphology, surface roughness, material removal rate, surface elements, surface corrosion resistance, and surface hardness of TC4 titanium alloy were measured before and after polishing. Single-factor experiments were conducted to explore the effects of processing gap, electrolyte temperature, polishing voltage, and polishing time on TC4 titanium alloy surface quality. Optimal polishing parameters were determined to be an electrode gap of 12 mm, a voltage of 20 V, a temperature of 40 °C, and a polishing time of 10 minutes. The experimental results indicated that after electrochemical polishing, the workpiece surface roughness Ra decreased from 256 nm to 25 nm, achieving a roughness reduction rate of 90.23%, the material removal rate was 3.348 μm/min. The self-corrosion potential of the polished surface increased from -0.416 V to -0.375 V and the self-corrosion current density decreased from 4.447×10-7 A·cm-2 to 1.873×10-7 A·cm-2, the surface indentation hardness of the workpiece decreased from 5.8669 GPa to 4.6726 GPa. Based on the characterization of polished surface, the mechanism of electrochemical polishing of TC4 titanium alloy was proposed. The results demonstrate that electrochemical polishing of TC4 titanium alloy is highly efficient, involves no mechanical action, and causes no subsurface damage, making it a promising technology to meet medical requirements.

Geographical factor dominates spatial patterns of potential nitrate reduction rates in coastal wetland sediments in Fujian Province, China

Nitrate (NO3−) reduction is a key process governing the nitrogen (N) dynamics of coastal wetland sediments. Although the effects of environmental factors on the NO3− reduction mechanism in coastal wetland sediments have been examined in various studies, the effects of spatial variation in potential NO3− reduction processes in coastal wetland sediments and the factors driving geographical variation in these processes have not been widely examined. Here, we conducted research on surface sediment samples from four different vegetation types at six coastal wetland sites across two regions. We characterized potential rates of NO3− reduction processes (including denitrification (DF), anammox (ANA), and dissimilatory nitrate reduction to ammonium (DNRA)) using a15N tracer method. Additionally, we assessed the abundances of functional genes, and microbial community structure using high-throughput sequencing, and metagenomic sequencing. In six wetland sites, the contribution ranges of DF, ANA, and DNRA to NO3− reduction were 38.43%–55.69%, 31.33%–45.65%, and 5.26%–17.11%, respectively, and potential NO3− reduction was mainly driven by N removal via gaseous N (DF+ANA). Significant spatial differences were observed in the structure of bacterial and fungal microbial communities, suggesting that geographical distance has a major effect on microbial community structure. Environmental factors and Functional gene abundances were significantly related to potential NO3− reduction processes, and physicochemical properties had a stronger effect on potential NO3− reduction processes than gene abundances. Factors showing significant differences across regions were the main drivers of variation in potential NO3− reduction processes. Overall, our study showed that sediment substrates and geographical environmental factors rather than the abundance of functional genes and vegetation types were the main indicators of potential NO3− reduction activities in coastal wetlands.

Translational pharmacokinetic-toxicodynamic model of myelosuppression for dose optimization in combination chemotherapy of capecitabine and oxaliplatin from rats to humans.

XELOX therapy, which comprises capecitabine and oxaliplatin, is the standard first-line chemotherapeutic regimen for colorectal cancer. However, its myelosuppressive effects pose challenges for its clinical management. Mathematical modeling combining pharmacokinetics (PK) and toxicodynamics (TD) is a promising approach for optimizing dosing strategies and reducing toxicity. This study aimed to develop a translational PK-TD model using rat data to inform dosing strategies and TD implications in humans. The rats were administered capecitabine, oxaliplatin, or XELOX combination regimen, and PK and TD data were collected. PK parameters were analyzed using sequential compartment analysis, whereas TD responses were assessed using Friberg's semi-physiological model. A toxicity intensity-based nomogram recommends optimal dosing strategies. Translational modeling techniques using the hybrid PK-TD model were employed to predict clinical responses. The PK-TD model successfully predicted the time-course profiles of hematological responses in rats following monotherapy and XELOX combination treatment. Interactive effects on lymphocytopenia were identified with the co-administration of capecitabine and oxaliplatin. A model-based recommended combination of the dose reduction rate for escaping severe lymphocytopenia was proposed as 40% and 60% doses of capecitabine and oxaliplatin, respectively. The current translational model techniques successfully simulated the time-course profiles of blood cell counts with confidence intervals in patients using rat data. Our study provides valuable insights into dose optimization strategies for each individual drug within the XELOX regimen and underscores the potential of translational modeling to improve patient outcomes. In addition to dose determination, these data will lay the groundwork for advancing drug development processes in oncology. Significance Statement This study introduced a novel translational modeling approach rooted in a rat PK-TD model to optimize dosing strategies for the XELOX regimen for colorectal cancer treatment. Our findings highlight the interactive effects on lymphocytopenia and suggest a toxicity intensity-based nomogram for dose reduction, thus advancing precision medicine. This translational modeling paradigm enhances our understanding of drug interactions, offering a tool to tailor dosing, minimize hematological toxicity, and improve therapeutic outcomes in patients undergoing XELOX therapy.

Multipath mitigation for GPS/Galileo/BDS-3 precise point positioning with overlap-frequency signals

The multipath effect is a major Global Navigation Satellite System (GNSS) error source due to its environment-dependent characteristic, which complicates its mitigation process for the high-rate determination of displacements. For instance, Sidereal Filtering (SF) and Multipath Hemispherical Map (MHM) require the observations spanning at least one full cycle of satellite orbit repeat period (e.g., ten days for Galileo navigation satellite system (Galileo) to reproduce the satellite geometry against ground stations. As a consequence, the practicability of SF and MHM is limited due to potential station-surrounding changes over a long period. In this study, we used the overlap-frequency signals on Global Positioning System (GPS) L1/L5, Galileo E1/E5a, and BeiDou-3 Navigation Satellite System (BDS-3) B1C/B2a to construct an interoperable MHM (i.e., MHM_GEC) across constellations to mitigate multipath more efficiently. We thus used 31 days of 1-Hz GPS/Galileo/BDS-3 data at 21 stations in Europe to compare this overlap-frequency MHM with those GNSS-specific MHMs (i.e., MHM_G for GPS, MHM_E for Galileo, and MHM_C for BDS-3), as well as SF. It is confirmed that the multipath effects on overlap-frequency signals are of a high spatial consistency across all GNSS. The mean reduction rate of applying MHM_GEC to GPS, Galileo, and BDS-3 carrier-phase residuals is 25%, 31%, and 28.5%, respectively, which are up to 25 percentage points higher than those of MHM_G, MHM_E, and MHM _C. Furthermore, the MHM_GEC constructed using 5 to 6 days of data can improve the positioning precision by 40%, outperforming the MHM_E, MHM_C, and SF using 10 days of data. Therefore, the interoperable MHM_GEC is more efficient in mitigating multipath effects for high-precision GNSS positioning.

Integrated use of the CA–Markov model and the Trends.Earth module to enhance the assessment of land cover degradation

This study aims to demonstrate the potential of assessing future land cover degradation status by combining the forecasting capabilities of the Cellular-Automata and Markov chain (CA-Markov) models in Idris Selva with the land cover degradation (LCD) model in the Trends.Earth module. The study focuses on the upper Zambezi Basin (UZB) in southern Africa, which is one of the regions with high rates of land degradation globally. Landsat satellite imagery is utilised to generate historical (1993–2023) land cover and land use (LCLU) maps for the UZB, while the global European Space Agency Climate Change Initiative (ESA CCI) LCLU maps are obtained from the Trends.Earth module. The CA-Markov chain model is employed to predict future LCLU changes between 2023 and 2043. The LCD model in the Trends.Earth module in QGIS 3.32.3 is then used to assess the historical and forecasted land cover degradation status. The findings reveal that land cover degradation maps produced from local LCLU classifications provide more detailed information compared to those produced from the global ESA CCI LCLU product. Between 2023 and 2043, the UZB is predicted to experience a net reduction of approximately 3.2 million hectares of forest cover, with an average annual reduction rate of − 0.13%. In terms of land cover degradation, the UZB is forecasted to remain generally stable, with 87% and 96% of the total land cover area expected to be stable during the periods 2023–2033 and 2033–2043, respectively, relative to the base years 2023 and 2033. Reduction in forest cover due to the expansion of grassland, human settlements, and cropland is projected to drive land cover degradation, while improvements in forest cover are anticipated through the conversion of grassland and cropland into forested areas. It appears that using locally produced LCLU with high-resolution images provides better assessments of land degradation in the Trends.Earth module than using global LCLU products. By leveraging the opportunities offered by models with capacity to predict LCLU such as the CA–Markov model and the capabilities of the LCD model, as evidenced in this study, valuable forecasted information can be effectively obtained for monitoring land cover degradation. This information can then be used to implement targeted interventions that align with the objective of realising the United Nations' land degradation neutral world target by 2030.

Characterization of anti-seizure medication reduction and discontinuation rates following epilepsy surgery

ObjectiveMany patients pursue epilepsy surgery with the hope of reducing or stopping anti-seizure medications (ASMs), in addition to reducing their seizure frequency and severity. While ASM decrease is primarily driven by surgical outcomes and patient preferences, preoperative estimates of meaningful ASM reduction or discontinuation are uncertain, especially when accounting for the various forking paths possible following intracranial EEG (iEEG), including resection, neuromodulation, or even the absence of further surgery. Here, we characterize in detail the ASM reduction in a large cohort of patients who underwent iEEG, facilitating proactive, early counseling for a complicated cohort considering surgical treatment. MethodsWe identified a multi-institutional cohort of patients who underwent iEEG between 2001 and 2022, with a minimum of two years follow-up. The total number of ASMs prescribed immediately prior to surgery, choice of investigation modality, and subsequent surgical treatment were extracted for each patient. Primary endpoints included decreases in ASM counts from preoperative baseline to various follow-up intervals. ResultsA total of 284 patients were followed for a median of 6.0 (range 2,22) years after iEEG surgery. Patients undergoing resection saw an average reduction of ∼ 0.5 ASMs. Patients undergoing neuromodulation saw no decrease and trended towards requiring increased ASM usage during long-term follow-up. Only patients undergoing resection were likely to completely discontinue all ASMs, with an increasing probability over time approaching ∼ 10 %. Up to half of resection patients saw ASM decreases, which was largely stable during long-term follow-up, whereas only a quarter of neuromodulation patients saw a reduction, though their ASM reduction decreased over time. ConclusionsWith the increasing use of stereotactic EEG and non-curative neuromodulation procedures, realistic estimates of ASM reduction and discontinuation should be considered preoperatively. Almost half of patients undergoing resective surgery can expect to reduce their ASMs, though only a tenth can expect to discontinue ASMs completely. If reduction is not seen early, it likely does not occur later during long-term follow-up. Less than a third of patients undergoing neuromodulation can expect ASM reduction, and instead most may require increased usage during long-term follow-up.

Spin Polarization Enhances the Catalytic Activity of Monolayer MoSe2 for Oxygen Reduction Reaction

The key factors in achieving high energy efficiency for proton exchange membrane fuel cells are reducing overpotential and increasing the oxygen reduction rate. Based on first-principles calculations, we induce H atom adsorption on 4 × 4 × 1 monolayer MoSe2 to induce spin polarization, thereby improving the catalytic performance. In the calculation of supercells, the band unfolding method is used to address the band folding effect in doped systems. Furthermore, it is evident from analyzing the unique energy band configuration of MoSe2 that a higher valley splitting value has better catalytic effects on the oxygen reduction reaction. We believe that the symmetries of the distinct adsorption site result in different overpotentials. In addition, when an even number of hydrogen atoms is adsorbed, the monolayer MoSe2 has no spin polarization. The spin can affect the electron transfer process and alter the hybrid energy with the reaction products, thereby regulating its catalytic performance.

Chitosan-sunflower meal biochar hydrogel incorporated with green synthesized NiO nanoparticles for enhanced catalytic reduction of anthropogenic water pollutants.

Anthropogenic activities have been one of the crucial driving factors for water pollution globally, thereby warranting a sustainable strategy for its redressal. In this study, we have developed a hydrogel-biochar nanocomposite for catalytic reduction of water pollutants. To begin with, green synthesis of nickel oxide nanoparticles (NiO NPs) was accomplished from waste kinnow peel extract via the environmentally benign microwave method. The formation of NiO NPs was affirmed from different analytical techniques namely ultraviolet-visible (UV-Vis), Fourier transform infrared (FTIR), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM) and energy-dispersive spectroscopy (EDS). The FESEM images revealed spherical nature of NiO NPs. The average particle size was found to be 15.61 nm from XRD data. A novel hydrogel-biochar nanocomposite comprising the green NiO NPs, sunflower meal biochar and chitosan was prepared (Cs-biochar@ NiO) and explored as a nanocatalyst towards catalytic reduction of pollutants such as 4-nitrophenol, potassium hexacyanoferrate (III) and organic dyes methyl orange (MO), Congo red (CR), methylene blue (MB) in the presence of a reducing agent, i.e. NaBH4. Under optimized conditions, the reduction reactions were completed by 120 s and 60 s for 4-NP and potassium hexacyanoferrate (III) respectively and the rate constants were estimated to be 0.044 s-1 and 0.110 s-1. The rate of reduction was found to be faster for the dyes and the respective rate constants were 0.213 s-1 for MO, 0.213 s-1 for CR and 0.135 s-1 for MB. The assessment of the nanocatalyst in the reduction of binary dye systems depicted its selectivity towards the anionic dyes CR and MO. The nanocatalyst displayed effective reduction of dyes in real-water samples collected from different sources. Taken altogether, this study validates the design of sustainable hydrogel-biochar nanocatalyst for the efficient reduction of hazardous anthropogenic water pollutants.

Preoperative prediction of nodal status using clinical data and artificial intelligence derived mammogram features enabling abstention of sentinel lymph node biopsy in breast cancer

IntroductionPatients with clinically node-negative breast cancer have a negative sentinel lymph node status (pN0) in approximately 75% of cases and the necessity of routine surgical nodal staging by sentinel lymph node biopsy (SLNB) has been questioned. Previous prediction models for pN0 have included postoperative variables, thus defeating their purpose to spare patients non-beneficial axillary surgery. We aimed to develop a preoperative prediction model for pN0 and to evaluate the contribution of mammographic breast density and mammogram features derived by artificial intelligence for de-escalation of SLNB.Materials and methodsThis retrospective cohort study included 755 women with primary breast cancer. Mammograms were analyzed by commercially available artificial intelligence and automated systems. The additional predictive value of features was evaluated using logistic regression models including preoperative clinical variables and radiological tumor size. The final model was internally validated using bootstrap and externally validated in a separate cohort. A nomogram for prediction of pN0 was developed. The correlation between pathological tumor size and the preoperative radiological tumor size was calculated.ResultsRadiological tumor size was the strongest predictor of pN0 and included in a preoperative prediction model displaying an area under the curve of 0.68 (95% confidence interval: 0.63–0.72) in internal validation and 0.64 (95% confidence interval: 0.59–0.69) in external validation. Although the addition of mammographic features did not improve discrimination, the prediction model provided a 21% SLNB reduction rate when a false negative rate of 10% was accepted, reflecting the accepted false negative rate of SLNB.ConclusionThis study shows that the preoperatively available radiological tumor size might replace pathological tumor size as a key predictor in a preoperative prediction model for pN0. While the overall performance was not improved by mammographic features, one in five patients could be omitted from axillary surgery by applying the preoperative prediction model for nodal status. The nomogram visualizing the model could support preoperative patient-centered decision-making on the management of the axilla.

Production and performance evaluation of chitosan/collagen/honey nanofibrous membranes for wound dressing applications

Persistent bacterial infections are the leading risk factor that complicates the healing of chronic wounds. In this work, we formulate mixtures of polyvinyl alcohol (P), chitosan (CH), collagen (C), and honey (H) to produce nanofibrous membranes with healing properties. The honey effect at concentrations of 0 % (PCH and PCHC), 5 % (PCHC-5H), 10 % (PCHC-10H), and 15 % (PCHC-15H) on the physicochemical, antibacterial, and biological properties of the developed nanofibers was investigated. Morphological analysis by SEM demonstrated that PCH and PCHC nanofibers had a uniform and homogeneous distribution on their surfaces. However, the increase in honey content increased the fiber diameter (118.11–420.10) and drastically reduced the porosity of the membranes (15.79–92.62 nm). The addition of honey reduces the water vapor transmission rate (WVTR) and the adsorption properties of the membranes. Mechanical tests revealed that nanofibers were more flexible and elastic when honey was added, specifically the PCHC-15H nanofibers with the lowest modulus of elasticity (15 MPa) and the highest elongation at break (220 %). Also, honey significantly improved the antibacterial efficiency of the nanofibers, mainly PCHC-15H nanofibers, which presented the best bacterial reduction rates against Staphylococcus aureus (59.84 %), Pseudomonas aeruginosa (47.27 %), Escherichia coli (65.07 %), and Listeria monocytogenes (49.58 %). In vitro tests with cell cultures suggest that nanofibers were not cytotoxic and exhibited excellent biocompatibility with human fibroblasts (HFb) and keratinocytes (HaCaT), since all treatments showed higher or similar cell viability as opposed to the cell control. Based on the findings, PVA-chitosan-collagen-honey nanofibrous membranes have promise as an antibacterial dressing substitute.

Investigation of the Penetration Capability of Exponential Bullets into Two Typical TPMS Lattice Structures

The TPMS lattice structure is widely utilized in the military UAV, and effectively striking the TPMS structure holds significant reference value in bullet design. This paper conducts a numerical simulation to study the penetration performance of exponential curve bullets with varying power exponents on the TPMS structures of Gyroid and Diamond. MATLAB is employed to design the TPMS lattice structure, and ABAQUS is used for numerical simulation. The results indicate that the velocity reduction rate of the bullet initially increases and then decreases with an increase in power exponent. Moreover, a larger power exponent leads to a smaller residual velocity and kinetic energy of the bullet. Different cell structures have a substantial impact on residual kinetic energy, with the Gyroid structure exhibiting significantly greater residual kinetic energy compared to the Diamond structure, resulting in an average kinetic energy difference of 15.429[Formula: see text]J. The research shows that the curve of the arc part of the warhead has a great influence on the penetration performance. When designing the bullet used to hit the military UAV, the warhead with a large curvature change within a certain range can be considered.

Antibacterial Food Packaging Containing Stably Dispersed Cu Nanoparticles Synthesized by Novel Physical Vapor Deposition Process

Polymeric materials were modified by nanoparticles on powder (NPP) facility in which metal nanoparticles (Cu, Cu/Zn alloy etc.) are formed on the surface of rotating carrier powder by using physical vapor deposition process untile the metal content reached 0.3wt.%. The polymer material with metal nanoparticles deposited on their surface through the NPP process was then added to the raw polymer material at a ratio of 1 to 9. The mixture of modified polymer powder and raw polymer powder were then processd into a film by conventional processes such as hot melting, extrusion, T-die, and antibacterial characteristics of the film were investigated. We ultimately manufactured antibacterial food package using the film and conducted preservation test for two weeks at room temperature. Since food package containing 0.03wt.% Cu nanoparticles showed over 99.9% bacteria reduction rate, it slowed down the progress of deterioration significantly compared to conventional packages. For safety evaluation, the amount of copper released out was analyzed and a cytotoxicity test was also conducted.

Hydrothermal Dissolution of Reactive Silica Materials in Sodium Hydroxide Lyes

AbstractReactive silica materials like vitreous silica or cristobalite were dissolved at temperatures between 100 and 220 °C for up to 7 days in NaOH lyes. The liquid and solid reaction products were analyzed chemically. Sodium water glasses with silica concentrations of up to 27 wt % and molar SiO2/Na2O ratios of up to 3.7 were obtained. The experimental data were evaluated by an approximation algorithm. The initial size reduction rates ranged between 0.1 and 7 µm h−1, depending on the temperature and the choice of starting materials. Correlations between thermodynamics and kinetics were discussed.

Autocatalyzed Kinetics of 6-Electron Electroreduction of Iodic Acid Studied by Rotating Disk Electrode Technique

The 6-electron electrochemical reduction of IO3− to I− represents a breakthrough for the development of next-generation redox flow batteries, offering substantially higher energy densities for oxidizer storage. Our study reveals that on a glassy carbon (GC) electrode in acidic electrolytes, HIO3 undergoes an autocatalyzed electrochemical reduction to I−. This process is mediated by the formation of a thin iodine layer on the electrode, acting as an intermediate and a catalyst. Under steady-state conditions, the iodine layer forms via a comproportionation reaction (HIO3 + I− + 5H+ = I2 (s) + 3H2O). Initially, the iodine layer is generated through the slow direct electrochemical reduction of HIO3 on pristine GC. Once established, this layer significantly enhances the rate of iodate reduction. On voltammetry curves, it is clearly observable as a step-wise current surge to reach a plateau. The limiting current density on the GC seemingly aligns with the Levich equation, varying with the RDE rotation rate. Earlier, we demonstrated the electrochemical oxidation of I− back to HIO3 using an H2/HIO3 flow cell, showcasing a full cycle that underpins the feasibility of this approach for energy storage. This study advances the understanding of iodate electroreduction and underscores its role in enhancing the capacity of next-generation energy storage systems.

Quantum optimization algorithm based on multistep quantum computation

Abstract We present a quantum algorithm for finding the minimum of a function based on multistep quantum computation, and apply the algorithm for solving optimization problems with continuous variables. We construct the state space of the problem by discretizing the variables of the problem, and divide the state space according to the function values of the vectors of the state space. By comparing the function values of the vectors with a series of threshold values in decreasing order, we construct a sequence of Hamiltonians where the search space of a Hamiltonian is nested in that of the previous one. By applying a multistep quantum computation process for&amp;#xD;finding the ground state of the last Hamiltonian, the optimal vector of the state space of the problem is located in a small search space and can be determined efficiently. One of the most difficult problems in optimization algorithms is that a trial vector is trapped in a deep local minimum while the global minimum is missed, this problem can be alleviated in our algorithm and the run time is proportional to the number of the steps of the algorithm, provided that the reduction rate of the search spaces is polynomial large. We discuss the implementation of the algorithm and test the algorithm for some test functions.

Quantum optimization algorithm based on multistep quantum computation

Abstract We present a quantum algorithm for finding the minimum of a function based on multistep quantum computation, and apply the algorithm for solving optimization problems with continuous variables. We construct the state space of the problem by discretizing the variables of the problem, and divide the state space according to the function values of the vectors of the state space. By comparing the function values of the vectors with a series of threshold values in decreasing order, we construct a sequence of Hamiltonians where the search space of a Hamiltonian is nested in that of the previous one. By applying a multistep quantum computation process for&amp;#xD;finding the ground state of the last Hamiltonian, the optimal vector of the state space of the problem is located in a small search space and can be determined efficiently. One of the most difficult problems in optimization algorithms is that a trial vector is trapped in a deep local minimum while the global minimum is missed, this problem can be alleviated in our algorithm and the run time is proportional to the number of the steps of the algorithm, provided that the reduction rate of the search spaces is polynomial large. We discuss the implementation of the algorithm and test the algorithm for some test functions.

Vascular Blush Size Reduction, Technical Success and Safety of Intra-Arterial Embolization for Glomus Jugular Tumors: A Retrospective Cohort Study

Background: Glomus jugulare tumors are slow-growing neuroendocrine paragangliomas located in the temporal bone. Surgical excision, the standard treatment option, carries a high risk of intraoperative bleeding due to the vascular nature of these lesions. To mitigate this risk, current practice involves performing intra-arterial embolization prior to surgery. Objectives: The objectives of this study were to assess postintervention safety, technical success, and the rate of vascular blush size reduction in a group of patients with Glomus jugulare tumors who were planned for surgery and underwent pre-surgical intra-arterial embolization of the lesion. Patients and Methods: In this retrospective cohort study, all patients who underwent intravascular embolization for Glomus jugulare tumors from 2017 onwards were included. The study comprised 61 patients scheduled for surgery following embolization. All embolizations were completed in one session and were primarily performed via external carotid artery branches using polyvinyl alcohol (PVA). The size of the vascular blush was measured before and after embolization in coronal, supero-inferior sagittal, and antero-posterior sagittal planes, and the rate of blush size reduction was calculated. Procedure complications, including post-embolization pain, were recorded. In the statistical analysis, vascular blush size was compared between pre- and post-embolization times. Results: Pain after embolization was reported in 11 patients (18%). There were no other complications among the patients, indicating a high level of safety. All procedures were technically successful. The mean pre-embolization vascular blush diameter in the coronal plane was 29.1 ± 13.3 mm, which decreased to 16.8 ± 7.1 mm after embolization, representing an average diameter reduction of 38.5% (P &lt; 0.001). Conclusions: The findings of this retrospective cohort study suggest that intra-arterial embolization for Glomus jugulare tumors is safe, technically successful, and effective in reducing vascular blush before surgery.