High Performance Research Articles

Development of a new high performance liquid chromatography-fluorescence detection (HPLC-FL) method for the determination of biogenic amines in frozen fish sold in markets

<p class="MsoNormal" style="text-align:justify;line-height:200%"><span lang="EN-GB" style="font-size:12.0pt;line-height:200%;font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-GB;mso-fareast-language: ZH-CN">It is now possible to measure the biogenic amines histamine, tryptamine, putrescine, cadaverine, spermidine, spermine, and tyramine in an easy, accurate, and selective manner. Dansyl chloride was used to purify the analytes, and high-performance liquid chromatography with detection of fluorescence was utilized for separating them. This is a useful method for identifying and measuring biogenic amines in fish samples in a selective manner. The C18 (250 mm × 4.6 mm × 5 μm) column was used for the separation by chromatography. A mobile phase comprising acetonitrile and phosphate buffer (pH=4.0) (60:40, v/v) was used at an average flow rate of 0.9 mL/min. The technique relied on utilizing fluorescence detection to determine the derivative </span><span style="font-size:12.0pt;line-height:200%;font-family: "Times New Roman","serif"">(λ<sub>ex</sub>=230 nm, λ<sub>em</sub>= 350 nm) </span><span lang="EN-GB" style="font-size:12.0pt;line-height:200%;font-family:"Times New Roman","serif"; mso-fareast-font-family:"Times New Roman";mso-ansi-language:EN-GB;mso-fareast-language: ZH-CN">The suggested method's linearity was examined between 0.01 and 50 μg/mL </span><span style="font-size:12.0pt;line-height:200%;font-family:"Times New Roman","serif"">(r<sup>2</sup>=0.9996). </span><span lang="EN-GB" style="font-size:12.0pt;line-height:200%;font-family: "Times New Roman","serif";mso-fareast-font-family:"Times New Roman";mso-ansi-language: EN-GB;mso-fareast-language:ZH-CN">In compliance with ICH criteria, the method was verified with respect to accuracy, precision, repeatability, specifity, robustness, and detection and quantification limitations. The suggested technique has been effective in tracking the production of biogenic amines in specimens of fish that can be purchased frozen in marketplaces. It was discovered that the recommended approach worked well for routinely analyzing fish sample biogenic amine analyses.<o:p></o:p></span></p>

Leveraging Technological Research Methodologies to Improve Employee Performance in Organizations

The effective management of human resources in organizations is imperative for attaining optimum performance and eventual profitability. The increasingly competitive marketplace has compelled organizations to work towards attaining operational excellence, which heavily relies on the performance of its human resources. The adoption of cutting-edge research techniques has emerged as a pivotal strategy as organizations endeavour to achieve operational excellence. However, human resource managers have had to grapple with various challenges associated with the management of employee data generated and managed through digital platforms and tools, including issues such as data safety and security. This paper explores the intersection between tech-enhanced, advanced research methodologies and employee performance that is geared toward sustainable economic growth at the organizational level. Notably, the paper examines big data analytics as a tech-enhanced research tool leveraged by organizations in a bid to understand their human resource and the best way they can be managed to ensure high performance, which translates into high productivity and competitiveness in their respective sector or industries. In particular, this paper conducts a comprehensive review of recent extant literature regarding the adoption of big data analytics, which has revolutionized how organizations collect and analyze vast amounts of data, thereby enhancing their decision-making process. The reviewed literature focused on human resource aspects such as recruitment and selection; employee development; employee performance management and compensation structures. The review revealed that big data has emerged as a critical tool for managing large volumes of data that are acquired via numerous digital tools as organizations grapple with the integration of technology into their human resource practices. The technologies have helped human resource managers with these structured and unstructured data, they gain insight that in turn, guides their decision-making processes and improves the overall operational efficiency. The review outlines the transformative potential of integrating big data tools with human resource management and recommends that organizations should invest in these tools to enhance their recruitment and selection, employee development, performance measurement and upgrade their compensation structure.

Optimizing Forward Drop and Reverse Leakage Trade‐Off in AlGaN/GaN Lateral Diode with Schottky‐Metal‐Insulator‐Semiconductor Cascode Anode

Herein, AlGaN/GaN lateral diode with the Schottky‐Metal‐Insulator‐Semiconductor (MIS) cascode anode (CALD) to optimize forward voltage drop (VF) and reverse leakage current (ILEAK) trade‐off is proposed. In the CALD device, a normally‐on Ni/HfO2/AlGaN MIS‐controlled channel as well as a lateral Ni/GaN Schottky contact are cascoded at the anode. The designed normally‐on MIS‐controlled channel has high electron concentration at forward bias and prevents high electric‐field at reverse bias, which ensure both low VF and low ILEAK. Meanwhile, the Ni/GaN Schottky contact with a high Schottky barrier height further suppresses the ILEAK. As a result, the fabricated CALD demonstrates an optimized VF–ILEAK trade‐off relationship, including a low VF of 1.6 V and a low ILEAK of 5.2 × 10−8 A mm−1 (at reverse voltage of 400 V), together with a high breakdown voltage (BV) >700 V. These high performances suggest that the CALD can be a promising candidate for GaN power diode applications requiring a better VF–ILEAK trade‐off.

Intensive study, tuning and modification of reactive routing approach to improve flat FANET performance in data collection scenario.

The Flying Ad-hoc Network (FANET) can be defined as the Ad-hoc network that connects unmanned aerial vehicles flying in the space with each other and with a ground base station. However, the 3D movement of these drones with higher speeds results in a network of highly dynamic topology and intermittent connections, making the standard Ad Hoc routing protocols are not suitable for FANET. The approaches followed to address this issue include designing from scratch a routing protocol specific to FANET or modifying the existing protocols. From the view point of reliability, accuracy, and time, it is preferable to base the work on a protocol standard. But before amending the standard, tuning its performance and applying it under suitable conditions may be satisfactory for the new use. Therefore, this work considers flat FANET of fully mission-controlled drones and performs an extensive parametric simulation study to determine the best conditions and parameters' values for applying the popular Ad Hoc On-demand Distance Vector (AODV) to it. After deducing the recommended operating environment (FAODVN-OE), some examples of amendments were suggested to further improve the performance. It was found that the modified FAODVN-OE achieves high performance compared to the default standard in terms of jitter and delay. It helped reduce jitter and delay by an average of 93.2% and 83.8%, respectively, while exhausting less energy; however the network experiences a 24.5% reduction in packet delivery ratio.

Green synthesis of novel Z-scheme SnS2/HAp nanocomposite using Ocimum tenuiflorum leaf extract and investigation of its photocatalytic activity.

The present study focuses on the green synthesis of a novel Z-scheme SnS₂/HAp photocatalyst using Ocimum tenuiflorum (tulsi) leaf extract as a stabilizing agent. This approach not only emphasizes sustainability but also adds value to waste by extracting hydroxyapatite (HAp) from Labeo rohita fish scales, addressing the challenge of their disposal. The synthesized photocatalyst was thoroughly characterized using a range of analytical techniques to evaluate its crystal structure, optical properties, morphology, and elemental composition. The photocatalytic activity of the SnS₂/HAp composite was assessed through the degradation of gentian violet (GV) dye, a representative organic pollutant. Various reaction parameters were optimized to enhance the degradation efficiency, and the photocatalyst's performance was further tested across different water matrices. Under optimal conditions, the SnS₂/HAp photocatalyst achieved a maximum photodegradation efficiency of 97.49% with a rate constant of 0.0494min- 1 for GV dye. Additionally, it exhibited an efficiency greater than 70% against other emerging pollutants via advanced oxidation processes (AOP). The enhanced photocatalytic activity was attributed to the formation of a Z-Scheme heterojunction between SnS2 and HAp, which enhanced the charge separation efficiency and delayed the charge recombination. The study also demonstrated the photocatalyst's remarkable reusability, maintaining high performance over five cycles and across various water environments. This highlights its potential as a sustainable solution for the removal of organic pollutants from aqueous streams. Finally, a Z-scheme electron transport mechanism is proposed to explain the photodegradation process of GV dye using the SnS₂/HAp photocatalyst.

Study on the Performance of a Novel Double-Section Full-Open Absorption Heat Pump for Flue Gas Waste Heat Recovery

Open absorption heat pumps are considered one of the most promising methods for efficiently utilizing low-grade waste heat, reducing energy consumption, and lowering greenhouse gas emissions. However, traditional heat pumps have significant limitations in the range of flue gas temperatures they can recover, and their relatively low system performance further restricts practical applications. In this study, we propose a novel double-section full-open absorption heat pump driven by flue gas from the desulfurization tower. By designing the absorber with a double-layer structure, the system can recover more latent and sensible heat from the flue gas, significantly enhancing its thermal recovery capability. Additionally, replacing the traditional LiBr/H2O working pair with LiCl/H2O significantly reduces the risks of solution crystallization and equipment corrosion. Through comprehensive research, the strengths and weaknesses of the system were explored. The results indicate that this system effectively recovers flue gas waste heat within the temperature range of 30–70 °C. Specifically, at a flue gas temperature of 70 °C and a flow rate of 3 kg/s, the system achieves a COP of 1.838, along with a heating capacity of 158.83 kW and a ROI of 34.1%. These metrics demonstrate that the system not only delivers high performance but also exhibits excellent economic viability. Additionally, when the solution temperature is lowered to 10 °C, the system’s maximum COP reaches 1.96, reflecting a significant 30.67% improvement over traditional heat pumps. These findings highlight the system’s potential for application in coal-fired power plants, where varying levels of power output can benefit from enhanced thermal recovery and efficiency.

Odor lateralization test is insensitive to small degrees of intranasal trigeminal activation.

Odors with prominent trigeminal compounds are more easily localized than purely olfactory ones. However, it is still unclear whether adding a small amount of a trigeminal compound to an olfactory odor significantly improves lateralization performance. We included 81 healthy adults aged 25.4 ± 4.8 years to complete odor lateralization tasks using 12 odors: two "olfactory", two "trigeminal" odors, and eight odor mixtures at two low concentrations of "trigeminal" odors (4%, 8%). This task utilized a "Squeezer" delivering odor or air to either nostril, and participants indicated which nostril received the odor. Evaluations also included olfactory function, odor intensity ratings, and individual olfactory importance. Degrees of trigeminal compounds significantly affected lateralization performance (F = 82.32, p < 0.001), with 100% irritants showing higher performance than 0%, 4%, and 8% irritants (p's < 0.001), while no significant differences were found between odors with 0%, 4%, and 8% irritants (p's > 0.05). Chi-square tests confirmed higher percentages of above-chance lateralization with 100% irritants than with 0%, 4%, and 8% irritants (χ2 = 30.89 to 47.33, p's < 0.001). Adding a small amount of a trigeminal compound to a selective olfactory odor does not significantly improve lateralization performance. Trigeminal lateralization likely follows an "accumulative" pattern rather than an "all or none" rule. With only 20 trials, the task may lack sensitivity to detect low levels of trigeminal irritation in selective olfactory odors, though it does not rule out trigeminal activation. The odor lateralization task can screen for odors with prominent trigeminal compounds by comparing group-level performance with that of purely olfactory odors. Future studies should use more ideal stimuli (e.g., PEA for olfactory, CO2 for trigeminal) to test the replicability of the results.

Broadband self-powered MoS2/PdSe2/WSe2 PSN heterojunction photodetector for high-performance optical imaging and communication

Two-dimensional (2D) semi-metal transition metal dichalcogenides (TMDs) have drawn significant attention for their distinctive physical properties. However, the inherent high dark current of these materials and the single structure of detectors hinder the further development of photodetectors with high performance. Here, we construct a PSN (p-type semiconductor/semi-metal/n-type semiconductor) architecture by sandwiching 2D semi-metal between two semiconductor layers. In this architecture, the top and bottom layers generate an internal built-in electric field, while the middle layer serves as an absorption layer for low-energy photons and facilitates the dissociation of photo-generated carriers. As a result, the heterojunction device demonstrates a wide spectrum optical response from visible to infrared light (405 nm to 1550 nm) without requiring an external voltage. Working in self-powered mode at room temperature, the device achieves a responsivity of 0.56 A/W, a detectivity of 5.63 × 1011 Jones, and a rapid response speed of 190/74 µs. Additionally, the device shows potential for applications in fast optical communication and multi-wavelength optical imaging. This work presents a novel approach for developing a new type of broadband, self-powered, high-performance miniaturized semi-metal-based photodetector.

Multi‐component coupling synergistic effect of resourced gold tailings on intumescent flame‐retardant polypropylene

AbstractAddressing the challenge of resource utilization of gold tailings. A high performance flame‐retardant fillers (K‐GTF) were prepared from gold tailings (GTF) by coupling and condensation for application to intumescent flame‐retardant polypropylene (PP). K‐GTF enhances the flame‐retardant, thermal stability, smoke suppression and mechanical properties of flame‐retardant PP through synergistic approach. The results show that K‐GTF demonstrates good flame‐retardant synergism, with PP composites containing 3% additive (PP/G3) exhibiting substantial flame‐retardant and smoke suppression characteristics. Notably, PP/G3 boasts a limiting oxygen index (LOI) of 36.1%, which is substantially higher than the 33.6% LOI of the flame‐retardant PP without added K‐GTF (PP/G0). Furthermore, the peak heat release rate (PHRR), peak smoke production rate (PSPR), total heat release rate (THR), and total smoke production (TSP) of PP/G3 were decreased by 32.5%, 26.9%, 31.6%, and 21.5%, respectively, as compared to PP/G0. According to the synergistic mechanism analysis, K‐GTF exerts a multi‐component coupling synergistic effect, which promotes the production of phosphorus‐rich cross‐linking structures in flame‐retardant PP materials during the combustion, thereby promotes the formation of stable, dense and intumescent char to further enhances the flame‐retardant performance of the PP materials. In terms of mechanical effects, the introduction of K‐GTF alleviates the issue of a substantial decline in mechanical properties typically caused by flame retardants in PP materials. This indicates that K‐GTF improves the compatibility between the flame retardants and the PP matrix, thereby enhancing the mechanical properties of the flame‐retardant PP materials.Highlights Successfully synthesis a flame‐retardant synergist (K‐GTF). K‐GTF has good synergistic flame‐retardant effect. K‐GTF substantially enhances char formation and thermal stability of flame‐retardant PP. K‐GTF exerts a multi‐component coupling synergistic effect. K‐GTF enhances the compatibility between the PP matrix and the flame retardants.

Predicting 24-hour intraocular pressure peaks and averages with machine learning

PurposePredicting 24-hour peak and average intraocular pressure (IOP) is essential for the diagnosis and management of glaucoma. This study aimed to develop and assess a machine learning model for predicting 24-hour peak and average IOP, leveraging advanced techniques to enhance prediction accuracy. We also aimed to identify relevant features and provide insights into the prediction results to better inform clinical practice.MethodsIn this retrospective study, electronic medical records from January 2014 to May 2024 were analyzed, incorporating 24-hour IOP monitoring data and patient characteristics. Predictive models based on five machine learning algorithms were trained and evaluated. Five time points (10:00 AM, 12:00 PM, 2:00 PM, 4:00 PM, and 6:00 PM) were tested to optimize prediction accuracy using their combinations. The model with the highest performance was selected, and feature importance was assessed using Shapley Additive Explanations.ResultsThis study included data from 517 patients (1,034 eyes). For predicting 24-hour peak IOP, the Random Forest Regression (RFR) model utilizing IOP values at 10:00 AM, 12:00 PM, 2:00 PM, and 4:00 PM achieved optimal performance: MSE 5.248, RMSE 2.291, MAE 1.694, and R2 0.823. For predicting 24-hour average IOP, the RFR model using IOP values at 10:00 AM, 12:00 PM, 4:00 PM, and 6:00 PM performed best: MSE 1.374, RMSE 1.172, MAE 0.869, and R2 0.918.ConclusionThe study developed machine learning models that predict 24-hour peak and average IOP. Specific time point combinations and the RFR algorithm were identified, which improved the accuracy of predicting 24-hour peak and average intraocular pressure. These findings provide the potential for more effective management and treatment strategies for glaucoma patients.

Super Chloride Ionic Conductivity in CsSnCl3‐Based Perovskite Compound and Its Application for Solid‐State Chloride Batteries

A CsSnCl3‐based solid electrolyte in which 5% of Sn sites are occupied by Y ions, CsSn0.95Y0.05Cl3.05 (CSYC), is developed using a one‐step mechanical ball‐milling method. CSYC exhibits a stable cubic perovskite crystal structure and a high chloride ion conductivity of 4.9 mS cm−1. The relative density of a CSYC pellet following cold pressing without heat treatment is 97.5%. The high relative density and ionic conductivity are considered to originate from the high mechanical plasticity of the pellets, as evidenced by a low Young's modulus of 8.2 GPa. An all‐solid‐state chloride ion battery cell using CSYC as the electrolyte, BiCl3 as the active cathode material, and Sn as the anode is confirmed to operate at room temperature. The cell shows a large initial discharge capacity of 178 mAh g−1, ≈70% utilization, and good capacity retention with a reversible capacity of 100 mAh g−1 after 40 cycles. The mechanical plasticity of CSYC is considered to be a major contributor to its high ionic conductivity and good battery cycling performance.

Pure copper coating by multibeam directed energy deposition with blue lasers for antimicrobial effect

Pure copper has antimicrobial effect, and the development of pure copper coating technology for product surfaces is needed to prevent the spread of infections. To achieve high antimicrobial performance in coatings, it is essential to form a coating with high copper purity and minimal defects. In this study, we attempted to form a pure copper coating layer on a SS304 substrate using multibeam directed energy deposition with blue diode lasers. In the coating layer formation process, multiple bead layers are overlapped to form a smooth surface and two different types of joining is conducted simultaneously: the similar material joining area with the remelted previous layer, and the dissimilar-material joining area with the substrate. However, it was challenging to conduct this process for SS304 and pure copper due to their significantly different thermal properties. Therefore, we varied the hatching distance to control the heat input to both joining areas and investigated the effect on the quality of the coating layer. The results showed that there is an optimal ratio of similar and dissimilar joining areas, at which a high-quality coating layer with few voids and low dilution is formed. Furthermore, antimicrobial tests showed that the pure copper coating layer formed in this study exhibited the antimicrobial performance equivalent to those of a pure copper plate.

Exploring the electronic, magnetic and thermoelectric properties of TbPtBi half-Heusler: DFT study

Abstract In this investigation, we employed density functional theory to scrutinize the structural, electronic, magnetic, thermoelectric, and phonon properties of the topological half-Heusler (HH) TbPtBi compound. The stable phonon dispersion spectrum affirms the dynamical stability of the compound. The inclusion of spin-orbit coupling (SOC) significantly influenced the compound’s electronic and thermoelectric properties. The density of state (DOS) confirmed the impact of SOC on the topologically non-trivial metallic behavior of TbPtBi under the equilibrium lattice constant. The SOC altered the DOS at the Fermi level, leading to band splitting and a notable 70% reduction in state density. The Tb-4f electrons in the compound induce total magnetization in AFM (−5.93 µB/cell) and FM (5.94 µB/cell) phases, while SOC eliminates this magnetization. The thermoelectric performance of TbPtBi under compressive and tensile strain has been systematically studied. The result indicate that compressive strain causes a notable increment in Seebeck coefficient and Power factor (20.4 × 1011 W K−2 m−1) of this compound at room temperature. High thermoelectric performance under compressive strain in the HH compound TbPtBi might open new avenues for investigating other topological thermoelectric materials.

Assessing groundwater level modelling using a 1-D convolutional neural network (CNN): linking model performances to geospatial and time series features

Abstract. Groundwater level (GWL) forecasting with machine learning has been widely studied due to its generally accurate results and low input data requirements. Furthermore, machine learning models for this purpose can be set up and trained quickly compared to the effort required for process-based numerical models. Despite demonstrating high performance at specific locations, applying the same model architecture to multiple sites across a regional area can lead to varying accuracies. The reasons behind this discrepancy in model performance have been scarcely examined in previous studies. Here, we explore the relationship between model performance and the geospatial and time series features of the sites. Using precipitation (P) and temperature (T) as predictors, we model monthly groundwater levels at approximately 500 observation wells in Lower Saxony, Germany, applying a 1-D convolutional neural network (CNN) with a fixed architecture and hyperparameters tuned for each time series individually. The GWL observations range from 21 to 71 years, resulting in variable test and training dataset time ranges. The performances are evaluated against selected geospatial characteristics (e.g. land cover, distance to waterworks, and leaf area index) and time series features (e.g. autocorrelation, flat spots, and number of peaks) using Pearson correlation coefficients. Results indicate that model performance is negatively influenced at sites near waterworks and densely vegetated areas. Longer subsequences of GWL measurements above or below the mean negatively impact the model accuracy. Besides, GWL time series containing more irregular patterns and with a higher number of peaks might lead to higher model performances, possibly due to a closer link with precipitation dynamics. As deep learning models are known to be black-box models missing the understanding of physical processes, our work provides new insights into how geospatial and time series features link to the input–output relationship of a GWL forecasting model.

3D-Printed high performance PEEK/CF-PEEK polymer composites—An investigation on microstructural characteristics and mechanical performance

This study investigates the microstructural characteristics and mechanical properties of 3D-printed polyether ether ketone (PEEK) and carbon fiber-reinforced PEEK (PEEK CF20) composites. Using a specifically optimized 3D printing process parametric condition, samples of pure PEEK and PEEK CF20 (containing 20% carbon fiber) were fabricated. Mechanical properties were estimated by tensile, flexural, and interlaminar shear strength (ILSS) tests, performed as per ISO and DIN standards. Results indicated that PEEK CF20 exhibits significantly enhanced mechanical properties compared to pure PEEK. The tensile strength of PEEK CF20 was 81.069 MPa, compared to 76.070 MPa for pure PEEK. Maximum load values were 1456.608 N for PEEK CF20 and 1286.137 N for pure PEEK, while ILSS values were 15.76 MPa for PEEK CF20 and 11.82 MPa for pure PEEK. Microstructural analysis through field emission scanning electron microscopy (FESEM) revealed that PEEK CF20 had better fiber-matrix bonding and fewer defects compared to pure PEEK.

PyCI: A Python-scriptable library for arbitrary determinant CI.

PyCI is a free and open-source Python library for setting up and running arbitrary determinant-driven configuration interaction (CI) computations, as well as their generalizations to cases where the coefficients of the determinant are nonlinear functions of optimizable parameters. PyCI also includes functionality for computing the residual correlation energy, along with the ability to compute spin-polarized one- and two-electron (transition) reduced density matrices. PyCI was originally intended to replace the abinitio quantum chemistry functionality in the HORTON library but emerged as a standalone research tool, primarily intended to aid in method development, while maintaining high performance so that it is suitable for practical calculations. To this end, PyCI is written in Python, adopting principles of modern software development, including comprehensive documentation, extensive testing, continuous integration/delivery protocols, and package management. Computationally intensive steps, notably operations related to generating Slater determinants and computing their expectation values, are delegated to low-level C++ code. This article marks the official release of the PyCI library, showcasing its functionality and scope.

The Reconstruction of Bi2Te4O11 Nanorods for Efficient and pH-universal Electrochemical CO2 Reduction.

The electrochemical CO2 reduction reaction (CO2RR) to generate chemical fuels such as formate presents a promising route to a carbon-neutral future. However, its practical application is hindered by the competing CO production and hydrogen evolution reaction (HER), as well as the lack of pH-universal catalysts. Here, Te-modified Bi nanorods (Te-Bi NRs) were synthesized through in situ reconstruction of Bi2Te4O11 NRs under the CO2RR condition. Our study illustrates that the complex reconstruction process of Bi2Te4O11 NRs during CO2RR could be decoupled into three distinct steps, i.e., the destruction of Bi2Te4O11, the formation of Te/Bi phases, and the dissolution of Te. The thus-obtained Te-Bi NRs exhibit remarkably high performance in CO2RR towards formate production, showing high activity, selectivity, and stability across all pH conditions (acidic, neutral, and alkaline). In a flow cell reactor under neutral, alkaline, or acidic conditions, the catalysts achieved HCOOH Faradaic efficiencies of up to 94.3 %, 96.4 %, and 91.0 %, respectively, at a high current density of 300 mA cm-2. Density functional theory calculations, along with operando spectral measurements, reveal that Te manipulates the Bi sites to an electron-deficient state, enhancing the adsorption strength of the *OCHO intermediate, and significantly suppressing the competing HER and CO production. This study highlights the substantial influence of catalyst reconstruction under operational conditions and offers insights into designing highly active and stable electrocatalysts towards CO2RR.

Developing high-performance oxygen electrodes for intermediate solid oxide cells (SOC) prepared by Ce0.8Gd0.2O2−δ backbone infiltration

Gd0.2Ce0.8O 2−δ (GDC) porous backbone infiltration with La0.6Sr0.4CoO3−δ (LSC), PrOx and LSC: PrOx as a composite oxygen electrode for intermediate solid oxide cells are conducted within the scope of this work. Samples were characterized using scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), and electrochemical impedance spectroscopy (EIS). A uniform distribution of the infiltrated material inside the backbone and at the electrolyte-backbone interface was achieved. EIS measurements on the prepared symmetrical samples showed electrode polarization resistance (Rp) values of 0.029 Ω.cm², 0.23 Ω.cm², and 0.44 Ω.cm² for LSC, LSC: PrOx, and PrOx at 600 °C, respectively. Long-term stability measurements at 600 °C for 100 h showed a slight increase in polarization resistance during the measurement period. Fuel cell measurements of commercial cells (Elcogen) with porous oxygen electrode consisting of GDC infiltrated with LSC showed an increase in power density compared to the reference cell with a value of 0.53 W.cm− 2 obtained at 600 °C. It is proven that infiltration via polymeric precursor into porous scaffolds as backbone oxygen electrode layer is effective and convenient method to develop high performance and stable solid oxide cells.

Deep Forest Modeling: An Interpretable Deep Learning Method for Mineral Prospectivity Mapping

AbstractAccurate mineral prediction is crucial for reducing costs and uncertainties in mineral discovery and extraction. The use of artificial intelligence and big data has advanced mineral prediction into intelligent forecasting. Machine learning methods have shown significant promise in enhancing outcomes. Currently, neural network‐based approaches dominate deep learning (DL), but they lack interpretability and have high modeling complexity, making them less effective for complex problems and time‐consuming. Deep Forest, an innovative DL paradigm, addresses these issues by dynamically adjusting complexity and providing importance assessments for predictive factors. This study focuses on the North American Cordillera, known for its rich geological data and potential for porphyry copper deposits (PCDs). Predictions are made using Deep Forest with factors like Euclidean distance between faults and magmatic rock, fault line density, gravity anomalies, and stream‐sediment geochemical data. Deep neural networks, random forest, convolutional neural networks, transformer model and graph convolutional networks are also used for comparison. Deep Forest shows high performance and can avoid the black box problem of DL without relying on other tools in DL, providing a new perspective for the development and application of other non‐neural network DL models for mineral prediction. Feature importance analysis shows that geological structure and magmatism significantly influence PCD prediction. Elevated levels of elements like Al, Co, and Cr in stream sediments help identify mineralization‐related alterations. These findings underscore Deep Forest's capability to accurately and efficiently guide mineral exploration, highlighting its potential as a promising approach for mineral prospecting.

Tilianin obtained from Agastache mexicana inhibits monoamine oxidase and modifies depressive behavior in rats.

Agastache mexicana is used in traditional medicine to treat anxiety, insomnia, pain, among others. In a previous study, the methanolic extract exerted anxiolytic and sedative effects, as observed behaviorally, associated with one of its major components, tilianin. To assess the effect produced by the extracts and tilianin obtained from Agastache mexicana on depressive-induced behavior model and on the activity of monoamine oxidases (MAOs). The depression experimental model consisted of the forced swimming test in rats. MAOs activity was evaluated in cortex and hippocampus from the tilianin and Agastache extracts treated rats using specific inhibitors for each isoform. The quantification of monoamines was carried out using an High Performance Liquid Chromatography method. An increase in the swimming time was observed in rodents treated with doses of 16 (226.6±5.5 seconds) and 50mg/kg (237.8±5.7 seconds) of tilianin, methanolic (260.4±3 seconds), and hydroalcoholic extracts (249.6±2.6 seconds) at 100mg/kg. MAOs activity was significantly decreased in brain tissue from animals treated with 16 and 50mg/kg of tilianin, methanolic, and hydroalcoholic extracts at 100mg/kg. The tilianin effect on monoamine oxidases inhibition is confirmed, suggesting its potential use in the treatment of certain neurological disorders.