Traditional Calculation Research Articles

Thickness-Dependent Gilbert Damping and Soft Magnetism in Metal/Co-Fe-B/Metal Sandwich Structure.

The achievement of the low Gilbert damping parameter in spin dynamic modulation is attractive for spintronic devices with low energy consumption and high speed. Metallic ferromagnetic alloy Co-Fe-B is a possible candidate due to its high compatibility with spintronic technologies. Here, we report thickness-dependent damping and soft magnetism in Co-Fe-B films sandwiched between two non-magnetic layers with Co-Fe-B films up to 50 nm thick. A non-monotonic variation of Co-Fe-B film damping with thickness is observed, which is in contrast to previously reported monotonic trends. The minimum damping and the corresponding Co-Fe-B thickness vary significantly among the different non-magnetic layer series, indicating that the structure selection significantly alters the relative contributions of various damping mechanisms. Thus, we developed a quantitative method to distinguish intrinsic from extrinsic damping via ferromagnetic resonance measurements of thickness-dependent damping rather than the traditional numerical calculation method. By separating extrinsic and intrinsic damping, each mechanism affecting the total damping of Co-Fe-B films in sandwich structures is analyzed in detail. Our findings have revealed that the thickness-dependent damping measurement is an effective tool for quantitatively investigating different damping mechanisms. This investigation provides an understanding of underlying mechanisms and opens up avenues for achieving low damping in Co-Fe-B alloy film, which is beneficial for the applications in spintronic devices design and optimization.

In Situ Gas Content Prediction Method for Shale.

Shale gas is a typical unconventional energy source and recently has received great attention around the world. Unlike conventional natural gas, shale gas mainly exists in two forms: free state and adsorbed state. Therefore, geologists have proposed the concept of gas content. The traditional calculation methods of gas content can be summarized as on-site gas desorption, logging interpretation, isothermal adsorption, and so on. However, all of the methods mentioned above have their shortcomings. In situ gas content is a new concept in the calculation of the gas content. In this paper, the in situ gas content is defined as the gas content obtained by direct measurement of core gas production through experimental or mathematical simulation of original reservoir conditions. In this work, a method to calculate the in situ gas content of shale is provided, which includes two parts: numerical simulation of the coring process and a gas content experiment. Compared with previous gas content prediction methods, this article considers the influence of the temperature field on gas content both in mathematical modeling and experiments. Then, the gas content of the Longmaxi Formation shale in the Sichuan Basin was calculated using both methods as an example. The results show that (1) the numerical model was considered to be reliable by analyzing the effects of coring speed and permeability on the loss of gas; (2) the total gas content predicted by numerical simulation of the coring process and the gas content experiment are approximately equal, with values of 5.08 m3/t and 4.95 m3/t, respectively; (3) the total gas content of the USBM method is only 4.28 m3/t, which is significantly lower than the above methods. In summary, this study provides an in situ gas content prediction method for shale from both mathematical modeling and experiments. The mutual verification of theory and experiment makes this method highly reliable.

Improvement and application of the ecological footprint calculation Method—A case study of a Chinese university

The Ecological Footprint (EF) model is an effective tool for determining the resource consumption demand of human activities and whether natural assets are overutilized. The EF calculation method has certain theoretical and methodological importance for low-carbon campus construction. With the development of science and technology, some parameters in traditional EF calculation methods need to be modified. Based on the definition of fossil energy land and the latest average grain yield, this paper revised the calculation parameters of the EF method in six aspects: absorbed gas, electricity, agricultural products, aquatic products, livestock products, and paper. This paper applied the calculation method before and after improvement to Henan Polytechnic University. The EF of the campus according to calculations was 28,358.41 gha, and the per capita EF was 0.657 gha/person using the improved calculation method. Compared with the unimproved method, the improved method resulted in a substantial decrease in EFs, which can be explained by three reasons. First, the fossil energy land was divided into forestland and pasture land. Second, the average yield was recalculated using the latest statistical database of the United Nations Food and Agriculture Organization. Finally, the utilization of waste pulp in the paper industry was considered. The calculated results were also analyzed and compared with those of other universities, Worldwide Fund for Nature and Enhancing Universities’Sustainability Teaching and Practices through Ecological Footprint. The improved calculation results can better reflect the actual situation of the campus, improve the rationality of EF calculation, and provide a reference for EF at the campus level. Accordingly, the current situation of campus resource consumption can be explained, further providing a basis for proposing an EF reduction scheme for green campus construction.

Efficient Machine Learning Model Focusing on Active Sites for the Discovery of Bifunctional Oxygen Electrocatalysts in Binary Alloys.

The distinctive characteristics of alloy catalysts, encompassing composition, structure, and modifiable adsorption sites, present significant potential for the development of highly efficient electrocatalysts for oxygen evolution/reduction reactions [oxygen evolution reactions (OERs)/oxygen reduction reactions (ORRs)]. Machine learning (ML) methods can quickly establish the relationship between material features and catalytic activity, thus accelerating the development of alloy electrocatalysts. However, the current abundance of features presents a crucial challenge in selecting the most pertinent ones. In this study, we explored seven intrinsic features directly derived from the material's structure, with a specific focus on the chemical environment of active sites and their nearest neighbors. An accurate and efficient ML model to predict potential bifunctional oxygen electrocatalysts based on the intrinsic features of AB-type alloy active sites and intermediate free energies in the OERs/ORRs was established. These features possess clear physical and chemical meanings, closely linked to the electronic and geometric structures of active sites and neighboring atoms, thereby providing indispensable insights for the discovery of high-performance electrocatalysts. The ML model achieved R2 scores of 0.827, 0.913, and 0.711 for the predicted values of the three intermediate (OH, O, OOH) free energies, with corresponding mean absolute errors of 0.175, 0.242, and 0.200 eV, respectively. These results indicate that the ML model exhibits high accuracy in predicting the intermediate free energies. Furthermore, the ML model exhibited a prediction efficiency 150,000 times faster than traditional density functional theory calculations. This work will offer valuable insights and a framework for facilitating the rapid design of potential catalysts by ML methods.

Neoarchean clockwise P-T trajectories with evolution sequence from HP to UHT metamorphism during decompression: Evidence from petrology, mineralogy, and phase equilibria modeling of mafic granulites in the Sittampundi Complex, southern India

The dispute of tectonic environments such as subduction-collision setting versus extension tectonic setting, is a common phenomenon in the Precambrian. Despite the ongoing controversy, the pressure–temperature (P–T) paths recorded by metamorphic rocks are often used as an indicator for distinguishing between subduction-collision and extension tectonic settings in the early Precambrian. It is widely acknowledged that high-pressure (HP) granulites may have formed in crustal-thickening-dominated collision/subduction environment. Conversely, ultra-high temperature (UHT) granulites may have formed in the elevated mantle heat flux environment. The Sittampundi Complex (SC), where HP granulites and UHT granulites coexist, plays a crucial role in unraveling the late-Neoarchean to early-Paleoproterozoic tectonic evolution of northern South Granulite-facies Terrane (NSGT) of India. In this study, we present new P–T paths from three HP mafic granulites overprinted by UHT granulite facies metamorphism in the SC and provide more data to unravel metamorphic evolution of NSGT. Phase equilibria modeling and traditional thermobarometry calculations illustrated that clockwise metamorphic pressure–temperature (P–T) paths were yielded, passing from 3.3 to 9.3 kbar/631–669 °C (prograde stage) through 11.9–18.5 kbar/828–986 °C (pressure-peak stage), and through 6.6–11.1 kbar/1019–1085 °C (temperature-peak stage) to 4.4–8.1 kbar/845–930 °C (retrograde stage). The HP mafic granulites exhibit clockwise P–T paths with a considerable heating process (ca. 150–180 °C) from the pressure-peak stage (HP-granulite sub-facies) to the temperature-peak stage (UHT-granulite sub-facies). Zircon U-Pb dating yields an age of 2513 ± 29 indicates that the metamorphism took place in the Neoarchean. The uncommon P–T paths from Neoarchean HP granulite in the NSGT suggest that during late Neoarchean, the NSGT had experienced the crustal-thickening dominated collision/subduction (assembly of micro-blocks of Dharwar Craton) and subsequently mantle diapirism process.

Calculation Method of the Phase Recovery of Gas Cap Reservoir with Bottom Water

In gas cap reservoirs underlain by bottom water, the connection between the reservoir and the aquifer leads to an increasing invasion of bottom water as reservoir development progresses. The average formation pressure of the reservoir will change, and the separated phase recovery of the gas cap reservoir with bottom water will be affected by the change in the average formation pressure. The traditional average formation pressure calculation formulas do not consider the water influx, so the accurate calculation of separated recovery cannot be obtained by those calculation methods. The development of gas cap reservoirs with bottom water presents several challenges, including the simultaneous production of oil and gas, undetermined rates of bottom water influx, and uncertain formation pressure and gas-to-oil ratios. These factors contribute to substantial discrepancies between theoretical calculations and actual observations. A more accurate and comprehensive approach is required to address these issues and enable a precise determination of the phase separated recovery in gas cap reservoirs with bottom water. The volume-deficit method is integrated with the Fetkovich quasi-steady state method for water influx. The water-influx prediction is incorporated into the material balance equation, which is further refined by introducing the Fetkovich model to enhance the estimation of the average formation pressure. The average formation pressure, once determined, is utilized in conjunction with the established relationships among this pressure, surface oil production, gas production, and the dissolved gas–oil ratio. Through the application of mass conservation principles, the varying degree of phase recovery, as influenced by fluctuations in the average formation pressure, is calculated. The precision of this refined method has been validated by a comparison with outcomes generated by simulation software. The results reveal a commendable accuracy: an error in the average formation pressure calculation is found to be merely 2.61%, while the errors in recovery degrees for gas cap gas, dissolved gas, and oil-rim oil are recorded at 2.73%, 2.94%, and 1.28%, respectively. These minor discrepancies indicate a good level of consistency and affirm the reliability of this advanced methodology, as demonstrated by passive assessments. This paper provides a method to accurately calculate the phase recovery without some oil- and gas-production data, which provides accurate data support for the actual production evaluation and subsequent development measures.

Semi-Analytical Formula for Estimating Variance Response of Megawatt Wind Turbine Tower Under Parked Condition

As the supporting structure of the wind turbine, the tower is important for the safety of the wind turbine. The accurate estimation of tower response is critical for the wind turbine tower design. In the preliminary design, lots of calculations are necessary to determine design parameters. The traditional time domain calculation suffers its low efficiency and hinders the design progress. To overcome this difficulty, the frequency domain analysis should be developed, especially for the large wind turbine which may be significantly affected by the aerodynamic damping. In this study, a semi-analytical formula for the variance of the tower base bending moment is developed from the frequency domain analysis at parked condition. First, based on the quasi-steady theory and the three-component (mean, background and resonant components) method commonly used in the wind-resistant design of structure, the analytical formula of the variance for the tower base bending moment is presented considering the three components of the wind velocity. Second, the analytical formula is corrected based on the comparison with the calculation result by FAST. In the correction, the empirical aerodynamic damping is adopted. Finally, the applicability of the developed semi-analytical formula is verified by 5[Formula: see text]MW, 10[Formula: see text]MW and 15[Formula: see text]MW wind turbines.

Geriatric nutrition risk index: a more powerful index identifying muscle mass loss in patients with rheumatoid arthritis.

To explore the association of geriatric nutrition risk index (GNRI), a traditional albumin-body weight calculation, with myopenia in patients with rheumatoid arthritis (RA) and compare its ability to identify myopenia with protein indicators. This cross-sectional study was carried out based on a Chinese RA cohort. Clinical data and protein indicators (including albumin, globulin, albumin to globulin ratio, prealbumin, hemoglobin) were collected. GNRI was estimated by serum albumin and body weight. Myopenia was indicated as muscle mass loss measured by bioelectric impedance analysis. There were 789 RA patients included with mean age 52.6 ± 12.6years and 77.6% female. There were 41.3%, 18.0%, 27.5%, 13.2% patients with no (GNRI > 98), low (GNRI 92 to ≤ 98), moderate (GNRI 82 to < 92), and major nutrition-related risk (GNRI < 82). There were 406 (51.5%) RA patients with myopenia, RA patients with major nutrition-related risk had the highest prevalence of myopenia (87.5% vs. 73.3% vs. 50.0% vs. 26.1%). Multivariate logistic analysis showed that compared with no risk, RA patients with low (OR = 3.23, 95% CI: 1.86-5.61), moderate (OR = 9.56, 95% CI: 5.70-16.01), and major nutrition-related risk (OR = 28.91, 95% CI: 13.54-61.71) were associated with higher prevalence of myopenia. Receiver operating characteristic curves showed that GNRI (AUC = 0.79) performed a better identifiable ability toward myopenia than serum albumin (AUC = 0.66) or others indicators (AUC range 0.59 to 0.65), respectively. GNRI, an objective and convenient albumin-weight index, may be preferable for identifying myopenia in RA patients. Key Points • We firstly elucidated the association of GNRI with muscle mass loss among RA patients, and compared its ability to identify muscle mass loss with serum albumin or other protein indicators. • Major nutrition-related risk identified by GNRI showed the highest risk of muscle mass loss, GNRI demonstrated a greater ability to identify myopenia in RA patients. which indicated GNRI was an objective and convenient albumin-weight index to identify myopenia in RA patients.

Evaluating Pavement Condition Index and Maintenance Management using Artificial Neural Networks

The pavement condition index (PCI) calculates pavement conditions based on current distresses. In traditional PCI calculation, a visual inspection method collects field data such distresses and stiffness. Data helps anticipate PCI values, a lengthy and difficult process. This research aims to create a simple, adaptable model that shows how PCIs, torments, and stiffness relate. Artificial neural networks (ANN) forecast PCI values for various parts, eliminating manual labour and specialized procedures. Based on distresses, the PCI estimates pavement conditions. For typical PCI intentions, a visual inspection device collects field data such distresses and stiffness. The data allows time-consuming and complicated PCI estimation. This study seeks to construct a simple, extensible model that links PCIs, torments, and rigidity. ANN prediction part PCI values, eliminating the need for manual labour and specialized technologies.

MicroPSCal: A MicroStation package for storage calculation of pumped storage power station during planning and designing

Pumped storage power plants can effectively guarantee the healthy development of energy and promote energy transformation and green development. The calculation accuracy is directly related to the flood control safety of the reservoir. The current storage calculation method of storage capacity is inefficient and complicated resulting in deviations between calculated values and actual storage capacity. The paper is devoted to the problem of efficiency and quality of capacity calculation in the planning and design stage of pumped storage power plants. A toolkit MicroPSCal is developed based on MicroStation software to simulate and calculate the corresponding storage capacity of different elevations and draw the storage capacity curve, which can be applied in the actual planning and design process of pumped storage power stations. The results of the calculation showed that this method can make full use of terrain accuracy to achieve high-precision calculation of reservoir capacity of different elevations and automatically draw high-quality storage curves. Compared with traditional storage capacity calculation methods, calculation time was reduced from 18 hours to 45 minutes. This method has obvious advantages and provides a new technical idea for storage capacity calculation during the planning and design of pumped storage power plants.

A Novel Spatial–Temporal Deep Learning Method for Metro Flow Prediction Considering External Factors and Periodicity

Predicting metro traffic flow is crucial for efficient urban planning and transit management. It enables cities to optimize resource allocation, reduce congestion, and enhance the overall commuter experience in rapidly urbanizing environments. Nevertheless, metro flow prediction is challenging due to the intricate spatial–temporal relationships inherent in the data and the varying influence of external factors. To model spatial–temporal correlations considering external factors, a novel spatial–temporal deep learning framework is proposed in this study. Firstly, mutual information is utilized to select the highly corrected stations of the examined station. Compared with the traditional correlation calculation methods, mutual information is particularly advantageous for analyzing nonlinear metro flow data. Secondly, metro flow data reflecting the historical trends from different time granularities are incorporated. Additionally, the external factor data that influence the metro flow are also considered. Finally, these multiple sources and dimensions of data are combined and fed into the deep neural network to capture the complex correlations of multi-dimensional data. Sufficient experiments are designed and conducted on the real dataset collected from Xi’an subway to verify the effectiveness of the proposed model. Experimental results are comprehensively analyzed according to the POI information around the subway station.

Modified intraocular lens power selection method according to biometric subgroups Eom IOL power calculator

This study evaluates the accuracy of a newly developed intraocular lens (IOL) power calculation method that applies four different IOL power calculation formulas according to 768 biometric subgroups based on keratometry, anterior chamber depth, and axial length. This retrospective cross-sectional study was conducted in at Korea University Ansan Hospital. A total of 1600 eyes from 1600 patients who underwent phacoemulsification and a ZCB00 IOL in-the-bag implantation were divided into two datasets: a reference dataset (1200 eyes) and a validation dataset (400 eyes). Using the reference dataset and the results of previous studies, the Eom IOL power calculator was developed using 768 biometric subgroups. The median absolute errors (MedAEs) and IOL Formula Performance Indexes (FPIs) of the Barrett Universal II, Haigis, Hoffer Q, Holladay 1, Ladas Super, SRK/T, and Eom formulas using the 400-eye validation dataset were compared. The MedAE of the Eom formula (0.22 D) was significantly smaller than that of the other four formulas, except for the Barrett Universal II and Ladas Super formulas (0.24 D and 0.23 D, respectively). The IOL FPI of the Eom formula was 0.553, which ranked first, followed by the Ladas Super (0.474), Barrett Universal II (0.470), Holladay 1 (0.444), Hoffer Q (0.396), Haigis (0.392), and SRK/T (0.361) formulas. In conclusion, the Eom IOL power calculator developed in this study demonstrated similar or slightly better accuracy than the Barrett Universal II and Ladas Super formulas and was superior to the four traditional IOL power calculation formulas.

Spatial patterns of suicide in Texas: The potential confounding effect of race and ethnicity composition

In the United States, suicide rates are much higher among non-Hispanic Whites than any other racial or ethnic group. However, the impact of this pattern for spatial analysis remains unclear. This article explores the relationship among race and ethnicity, suicide mortality, and space in Texas. We compare traditional age-adjusted mortality rates with standardized mortality ratios created using indirect race or ethnicity rate adjustment. Both measurements are compared in three different contexts: choropleth maps, Getis–Ord Gi* hot spot analysis, and location-allocation modeling. Results show dramatic changes across all three methods after race or ethnicity adjustment. Regions of higher-than-expected suicide mortality shift from the non-Hispanic White-dominated north to the majority-Hispanic south. Accordingly, the p-median location-allocation model moves selected service sites closer to minority strongholds in large urban areas and along the Texas–Mexico border. A closer look at individual counties shows that minority-majority counties may have suicide rates two to three times the state average (given the population composition) yet remain hidden by traditional rate calculation methods. Failing to account for race and ethnicity effects will inevitably draw attention and resources to non-Hispanic White communities. At a time when governments are making investments in both mental health and diversity initiatives, this hidden bias must be addressed.

Physics-Informed Neural Network (PINN) for Solving Frictional Contact Temperature and Inversely Evaluating Relevant Input Parameters

Ensuring precise prediction, monitoring, and control of frictional contact temperature is imperative for the design and operation of advanced equipment. Currently, the measurement of frictional contact temperature remains a formidable challenge, while the accuracy of simulation results from conventional numerical methods remains uncertain. In this study, a PINN model that incorporates physical information, such as partial differential equation (PDE) and boundary conditions, into neural networks is proposed to solve forward and inverse problems of frictional contact temperature. Compared to the traditional numerical calculation method, the preprocessing of the PINN is more convenient. Another noteworthy characteristic of the PINN is that it can combine data to obtain a more accurate temperature field and solve inverse problems to identify some unknown parameters. The experimental results substantiate that the PINN effectively resolves the forward problems of frictional contact temperature when provided with known input conditions. Additionally, the PINN demonstrates its ability to accurately predict the friction temperature field with an unknown input parameter, which is achieved by incorporating a limited quantity of easily measurable actual temperature data. The PINN can also be employed for the inverse identification of unknown parameters. Finally, the PINN exhibits potential in solving inverse problems associated with frictional contact temperature, even when multiple input parameters are unknown.

Next-level vegetation health index forecasting: A ConvLSTM study using MODIS Time Series

The Vegetation Health Index (VHI) is a metric used to assess the health and condition of vegetation, based on satellite-derived data. It offers a comprehensive indicator of stress or vigor, commonly used in agriculture, ecology, and environmental monitoring for forecasting changes in vegetation health. Despite its advantages, there are few studies on forecasting VHI as a future projection, particularly using up-to-date and effective machine learning methods. Hence, the primary objective of this study is to forecast VHI values by utilizing remotely sensed images. To achieve this objective, the study proposes employing a combined Convolutional Neural Network (CNN) and a specific type of Recurrent Neural Network (RNN) called Long Short-Term Memory (LSTM), known as ConvLSTM. The VHI time series images are calculated based on the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) data obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites. In addition to the traditional image-based calculation, the study suggests using global minimum and global maximum values (global scale) of NDVI and LST time series for calculating the VHI. The results of the study showed that the ConvLSTM with a 1-layer structure generally provided better forecasts than 2-layer and 3-layer structures. The average Root Mean Square Error (RMSE) values for the 1-step, 2-step, and 3-step ahead VHI forecasts were 0.025, 0.026, and 0.026, respectively, with each step representing an 8-day forecast horizon. Moreover, the proposed global scale model using the applied ConvLSTM structures outperformed the traditional VHI calculation method.

Research on optimization method of railway construction scheme based on multidimensional combination weighting and improved grey theory

The optimization of railway construction schemes is a complexity system engineering task with multiple dimensions, diverse conditional constraints, and multifaceted objective assessments. The decision-making and scheme evaluation entail subjectivity, randomness, and fuzziness. To address the comprehensive optimization challenge in construction schemes effectively and efficiently, we investigate an optimization method for railway construction schemes. This method is based on multi-dimensional combination weighting and improved grey theory. After analyzing the primary influencing factors, we established a railway construction plan optimization index system comprising 4 dimensions and 18 factors. The weight combination coefficient is determined using the pros and cons solution distance method, and the optimal weight set for the index is determined through the multi-dimensional combination weighting approach. Utilizing the method of superior and inferior solution distance coupled with grey theory, we ascertain the order of advantages and disadvantages for each construction scheme, subsequently achieving construction scheme optimization. To illustrate this, we employ the optimization process for a high-speed railway section in Guangxi as an exemplar. The verification results indicate that the gray relative closeness values for schemes A, B, and C are 0.7089, 0.4813, and 0.4463, respectively. Scheme A has the highest gray relative closeness value, thus making it the optimal route scheme. The optimal results obtained through this method align with the outcomes of expert validation and existing research, thereby validating the effectiveness and practicality of the model. By employing a multidimensional combination weighting method, the deficiencies of traditional indicator weight calculations are mitigated, resulting in indicator weights that are more reflective of the actual circumstances. At the same time, the application of improvements in the grey theory comprehensive evaluation method enables the integration and computation of indicator data for each construction plan. Through the intuitive representation of grey relative closeness, the advantages and disadvantages of each plan are effectively characterized. This enhances the scientific rigor and applicability of the railway construction plan optimization process. The research findings can serve as a reference for similar railway construction scheme selection problems in the future.

Validation of ChatGPT 3.5 as a Tool to Optimize Readability of Patient-facing Craniofacial Education Materials.

To address patient health literacy, the American Medical Association recommends that readability of patient education materials should not exceed a sixth grade reading level; the National Institutes of Health recommend no greater than an eigth-grade reading level. However, patient-facing materials in plastic surgery often remain at an above-recommended average reading level. The purpose of this study was to evaluate ChatGPT 3.5 as a tool for optimizing patient-facing craniofacial education materials. Eighteen patient-facing craniofacial education materials were evaluated for readability by a traditional calculator and ChatGPT 3.5. The resulting scores were compared. The original excerpts were then inputted to ChatGPT 3.5 and simplified by the artificial intelligence tool. The simplified excerpts were scored by the calculators. The difference in scores for the original excerpts between the online calculator and ChatGPT 3.5 were not significant (P = 0.441). Additionally, the simplified excerpts' scores were significantly lower than the originals (P < 0.001), and the mean of the simplified excerpts was 7.78, less than the maximum recommended 8. The use of ChatGPT 3.5 for simplification and readability analysis of patient-facing craniofacial materials is efficient and may help facilitate the conveyance of important health information. ChatGPT 3.5 rendered readability scores comparable to traditional readability calculators, in addition to excerpt-specific feedback. It was also able to simplify materials to the recommended grade levels. With human oversight, we validate this tool for readability analysis and simplification.

Evaluating the Role of Functional Groups in the Selective Capture of Ag(I) onto UiO-66-Type Metal-Organic Frameworks.

UiO-66-type metal-organic frameworks have been considered as promising adsorbents for capturing Ag(I) from wastewater. However, uncertainties persist regarding the specific absorptivity of individual functional groups to the UiO-66 framework structure. In this study, UiO-66-type metal-organic frameworks (UiO-66-X), featuring diverse functional groups (X = -(OH)2, -(COOH)2, -NO2, -NH2, -SO3H, -(SH)2), were synthesized in situ for Ag(I) capture. The findings revealed that functionalization significantly enhanced the adsorption capacity of Ag(I). Notably, quantitative analysis showed that 1 mol of -SH functional group onto the UiO-66 framework structure can adsorb 0.73 mol of Ag(I) ions, surpassing those of -COOH, -OH, -NH2, -SO3H, and -NO2 by 2.4-, 3.5-, 3.8-, 9.1-, and 24.3-fold, respectively. This represents the first assessment of the adsorption capacity of functionalized UiO-66 for Ag(I) based on each effective functional group, addressing limitations in traditional unit mass calculations. Further, the adsorption mechanism of UiO-66-X for selectively capturing Ag(I) was elucidated through experimental and theoretical analyses. Additionally, selectivity and practical applications confirm that UiO-66-(SH)2 exhibits strong anti-interference ability, whether in natural water bodies with complex compositions or in industrial wastewater under harsh conditions. We anticipate that this study will enhance our understanding of structure-performance dependencies of multivariate MOFs for designing novel adsorbents for Ag(I) capture.

A careful look at lipid nanoparticle characterization: analysis of benchmark formulations for encapsulation of RNA cargo size gradient

With the recent success of lipid nanoparticle (LNP) based SARS-CoV-2 mRNA vaccines, the potential for RNA therapeutics has gained widespread attention. LNPs are promising non-viral delivery vectors to protect and deliver delicate RNA therapeutics, which are ineffective and susceptible to degradation alone. While food and drug administration (FDA) approved formulations have shown significant promise, benchmark lipid formulations still require optimization and improvement. In addition, the translatability of these formulations for several different RNA cargo sizes has not been compared under the same conditions. Herein we analyze “gold standard” lipid formulations for encapsulation efficiency of various non-specific RNA cargo lengths representing antisense oligonucleotides (ASO), small interfering RNA (siRNA), RNA aptamers, and messenger RNA (mRNA), with lengths of 10 bases, 21 base pairs, 96 bases, 996 bases, and 1929 bases, respectively. We evaluate encapsulation efficiency as the percentage of input RNA encapsulated in the final LNP product (EEinput%), which shows discrepancy with the traditional calculation of encapsulation efficiency (EE%). EEinput% is shown to be < 50% for all formulations tested, when EE% is consistently > 85%. We also compared formulations for LNP size (Z-average) and polydispersity index (PDI). LNP size does not appear to be strongly influenced by cargo size, which is a counterintuitive finding. Thoughtful characterization of LNPs, in parallel with consideration of in vitro or in vivo behavior, will guide design and optimization for better understanding and improvement of future RNA therapeutics.

A Numerical Simulation Approach for Superheated Steam Flow during Multipoint Steam Injection in Horizontal Well

Superheated steam flow during multipoint steam injection technology has a good effect on improving the steam absorption profile of heavy oil thermal recovery wells, enhancing the production degree of horizontal section of thermal recovery wells, and enhancing oil recovery. Based on the structure of multipoint steam injection horizontal string, considering the characteristics of variable mass flow, pressure drop of steam-liquid two-phase flow, and throttling pressure difference of steam injection valve in the process of steam injection, this paper establishes the calculation model of various parameters of multipoint steam injection horizontal wellbore and calculates the distribution of steam injection rate, temperature, pressure gradient, and dryness along the section of multipoint steam injection in horizontal wellbore. The results show that the temperature and pressure decrease gradually from heel to toe, and the steam dryness decreases gradually. Considering the influence of throttle pressure difference of steam injection valve and pressure drop of gas-liquid two-phase flow in the wellbore, the traditional calculation model of steam injection thermodynamic parameters is optimized, and the optimization of wellbore structure and steam injection parameters is an effective method to achieve uniform steam injection in horizontal wells. The steam injection uniformity of horizontal wells can be effectively improved by adjusting the steam injection valve spacing and steam injection parameters. When the steam injection volume is 200 m3/d and the steam injection valve spacing is 20 m, a more stable steam injection effect can be obtained. The findings of this study can help for better understanding of improving the uniformity of steam injection and enhancing the recovery factor.