High Cost-effectiveness Research Articles

Enhancing optical properties and antimicrobial efficiency of polyamide-6 for medical applications

Abstract Polyamide-6 (PA-6) fibers are valued for their high mechanical strength and cost-effectiveness, but their inherent hydrophobicity restricts their applicability. To enhance functionality, a grafting process was applied at both low and high yields, enabling effective treatment of PA-6 fibers with silver nanoparticles (Ag NPs) to impart antimicrobial properties. An in-situ approach was employed to embed Ag NPs within the PA-6 fibers. The antimicrobial efficacy of the modified fibers was assessed against multidrug-resistant (MDR) pathogens, including methicillin-resistant, extended-spectrum beta-lactamase-producing, and quinolone- and carbapenem-resistant strains, using the shake flask method with optical density measurements. Fourier-transform infrared spectroscopy (FTIR) characterized the chemical changes associated with grafting and Ag NP incorporation. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDX) provided insights into fiber morphology and elemental composition, verifying the successful surface loading of Ag NPs. The Multiple Beam Fizeau fringes technique was used to evaluate optical properties, such as refractive index and birefringence, as indicators of structural changes. Although grafting reduced the optical properties initially, subsequent Ag NP treatment restored them. The PA-6-g-PAA 7%-t-Ag NPs fibers exhibited superior optical properties relative to PA-6-g-PAA 20.7%-t-Ag NPs fibers, though with lower antimicrobial impact on sensitive organisms. Conversely, PA-6-g-PAA 20.7%-t-Ag NPs showed significant antimicrobial activity against MDR pathogens. As a result, PA-6-g-PAA 20.7% is identified as the optimal choice, balancing effective antimicrobial properties with enhanced optical performance, suggesting its potential in antimicrobial fiber applications for medical use.

Tough and Stretchable Zwitterionic Eutectogels via Copolymerization-Induced Phase Separation in a Targeted Deep Eutectic Solvent.

Deep eutectic solvent (DES)-based eutectogels show significant promise for flexible sensors due to their high ionic conductivity, non-volatility, biocompatibility, and cost-effectiveness. However, achieving tough and stretchable eutectogels is challenging, as the highly polar DES tends to screen noncovalent bonds, such as hydrogen and ionic bonds, between polymer chains, limiting their mechanical strength. In this work, this issue is addressed by leveraging the limited solubility of zwitterionic polymers in a specific DES to induce phase separation, promoting dipole-dipole interactions between polymer chains. These interactions improve energy dissipation under mechanical stress, allowing the creation of tough and stretchable P(MAA-co-VIPS)/TBAC-EG eutectogels through a copolymerization-induced phase separation approach. Methacrylic acid (MAA) and sulfobetaine vinylimidazole (VIPS) are copolymerized within a tetrabutylammonium chloride-ethylene glycol (TBAC-EG) DES, resulting in a bicontinuous network. The bicontinuous structure consists of a PVIPS-rich phase that enhances toughness via dipole-dipole interactions, and a PMAA solvent-rich phase that enables high stretchability. The resulting eutectogel demonstrates excellent mechanical properties, including a strength of 1.76MPa, toughness of 16.61MJ m⁻3, and remarkable stretchability of 1293%, along with self-recovery, self-healing, and shape-memory capabilities. The zwitterionic polymer-specific DES design opens up broad application potential for these eutectogels in diverse fields.

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

The accurate and rapid diagnosis of bacterial vaginosis (BV) is crucial due to its high prevalence and association with serious health complications, including increased risk of sexually transmitted infections and adverse pregnancy outcomes. Although widely used, traditional diagnostic methods have significant limitations in subjectivity, complexity, and cost. The development of a novel diagnostic approach that integrates SERS with ML offers a promising solution. The CNN model's high prediction accuracy, cost-effectiveness, and extraordinary rapidity underscore its significant potential to enhance the diagnosis of BV in clinical settings. This method not only addresses the limitations of current diagnostic tools but also provides a more accessible and reliable option for healthcare providers, ultimately enhancing patient care and health outcomes.

Mitigating Zinc Dendrite Formation and Parasitic Side Reactions in Aqueous Zn-Ion Batteries Via Laser-Assisted Carbonization of Cu-PANI Films on Zn Anodes.

Aqueous zinc-ion batteries (ZIBs) are gaining attraction for large-scale energy storage systems due to their high safety, significant capacity, cost-effectiveness, and environmental friendliness. On the other hand, the development of aqueous ZIBs is restricted by the limited practical application of zinc (Zn) because of the high reactivity of Zn in aqueous electrolytes, which results in the severe dendrite growth and parasitic side reactions such as hydrogen evolution reaction (HER). In this study, heteroatom-doped carbon porous surface modification by laser-assisted carbonization of copper (Cu) doped polyaniline (PANI) is designed and fabricated on top of the Zn metal anode (c-Cu-PANI/Zn). The c-Cu-PANI surface-modified Zn anodes exhibit high electrochemical stability and performance during the Zn plating-stripping cycles and suppress the dendrite formation. The symmetrical cell and half-cell with the c-Cu-PANI/Zn anodes exhibit stable cycles for 6000h and 100% Coulombic efficiency for 2500 cycles, respectively. Moreover, the c-Cu-PANI/Zn║V2O5 cell delivers a high specific capacity of 319 mAh g-1 at 0.2 A g-1, which is significantly higher than that of the bare Zn║V2O5 cell (240 mAh g-1). It is believed that applying c-Cu-PANI as a surface modification can enhance the stability and reversibility of the Zn anodes, therefore accelerating the commercialization of ZIBs.

Adapting SAM2 Model from Natural Images for Tooth Segmentation in Dental Panoramic X-Ray Images

Dental panoramic X-ray imaging, due to its high cost-effectiveness and low radiation dose, has become a widely used diagnostic tool in dentistry. Accurate tooth segmentation is crucial for lesion analysis and treatment planning, helping dentists to quickly and precisely assess the condition of teeth. However, dental X-ray images often suffer from noise, low contrast, and overlapping anatomical structures, coupled with limited available datasets, leading traditional deep learning models to experience overfitting, which affects generalization ability. In addition, high-precision deep models typically require significant computational resources for inference, making deployment in real-world applications challenging. To address these challenges, this paper proposes a tooth segmentation method based on the pre-trained SAM2 model. We employ adapter modules to fine-tune the SAM2 model and introduce ScConv modules and gated attention mechanisms to enhance the model’s semantic understanding and multi-scale feature extraction capabilities for medical images. In terms of efficiency, we utilize knowledge distillation, using the fine-tuned SAM2 model as the teacher model for distilling knowledge to a smaller model named LightUNet. Experimental results on the UFBA-UESC dataset show that, in terms of performance, our model significantly outperforms the traditional UNet model in multiple metrics such as IoU, effectively improving segmentation accuracy and model robustness, particularly with limited sample datasets. In terms of efficiency, LightUNet achieves comparable performance to UNet, but with only 1.6% of its parameters and 24.0% of the inference time, demonstrating its feasibility for deployment on edge devices.

Recent Progress in Surface Acoustic Wave Sensors Based on Low-Dimensional Materials and Their Applications

Benefitting from high sensitivity, rapid response, and cost-effectiveness, surface acoustic wave (SAW) sensors have found extensive applications across various fields, including biomedical diagnostics, environmental monitoring, and industrial automation. Recently, low-dimensional materials have shown great potential in enhancing the performance of SAW sensors due to their exceptional physical, optical, and electronic properties. This review explores recent advancements in the fundamental mechanisms, design, fabrication and applications of SAW sensors based on low-dimensional materials. Specifically, the utilization of low-dimensional materials, including zero-, one- and two-dimensional materials, as sensing materials in SAW sensors are summarized. Their applications in SAW-based gas sensing, ultraviolet light sensing, humidity sensing, as well as biosensing are discussed. Furthermore, major challenges and future perspectives regarding employing low-dimensional materials to enhance SAW sensors are highlighted, providing valuable insights for future research and development in this field.

Rational Design of Porous Y2O3-MnOx/Carbon Heterostructures with Abundant Oxygen Vacancies for High-Efficiency and Ultrastable Zinc-Ion Storage.

Manganese oxides have been considered as the most competitive cathode materials for aqueous zinc-ion batteries (ZIBs) on account of their inherent safety, high operating voltage, environmental friendliness, and cost-effectiveness. Unfortunately, the manganese dissolution, inherently poor electronic conductivity, and the sluggish reaction kinetics of commercial manganese-based oxides severely hinder their practical applications. To address the above issues, we creatively developed hierarchical porous Y2O3-MnOx/C nanorods (named OV-YMO/C) with unique heterostructures and abundant oxygen vacancies via a facile MOF-assisted synthetic process and employed as the advanced cathode. Owing to the well-constructed porous structure, larger surface areas, abundant oxygen vacancies, and strong synergetic coupling effect at the heterogeneous interface, the as-obtained OV-YMO/C cathode exhibited a fascinating discharge capacity of 389.6 mAh g-1 at 0.1 A g-1. Simultaneously, it demonstrated remarkable rate performance (233 mAh g-1 at 4.0 A g-1) and cycling durability (90.6% capacity retention over 3000 cycles at 4.0 A g-1). The fabricated Zn//OV-YMO/C pouch cell could deliver superior flexibility and electrochemical stability under extreme bending conditions. Furthermore, the electrochemical reaction mechanism was comprehensively explored by kinetic analysis and density functional theory (DFT) calculations. The synergistic strategy by subtly combining the MOF-assisted approach, heterojunction engineering, and oxygen defects engineering provides valuable insights into the construction of cathode materials for high-rate and ultrastable aqueous ZIBs.

Mechanistic Insights and Technical Challenges in Sulfur-Based Batteries: A Comprehensive In Situ/Operando Monitoring Toolbox.

Batteries based on sulfur cathodes offer a promising energy storage solution due to their potential for high performance, cost-effectiveness, and sustainability. However, commercial viability is challenged by issues such as polysulfide migration, volume changes, uneven phase nucleation, limited ion transport, and sluggish sulfur redox kinetics. Addressing these challenges requires insights into the structural, morphological, and chemical evolution of phases, the associated volume changes and internal stresses, and ion and polysulfide diffusion within the battery. Such insights can only be obtained through real-time reaction monitoring within the battery's operational environment, supported by molecular dynamics simulations and advanced artificial intelligence-driven data analysis. This review provides an overview of in situ/operando techniques for real-time tracking of these processes in sulfur-based batteries and explores the integration of simulations with experimental data to provide a holistic understanding of the critical challenges, enabling advancements in their development and commercial adoption.

Caffeine-modified Magnetic Activated Carbon as Novel Bio Adsorbent for Removal of the Diazinon Pesticide in Aqueous Media

Introduction: In this study, a novel composite was prepared using a combination of nanotechnology and biotechnology. Method: This composite involved loading Fe3O4 NPs and immobilizing caffeine on the surface of activated carbon (CAF-MAC NCs), which was prepared from palm kernel source material. The adsorbent properties were characterized using FTIR, TEM, VSM, and TGA techniques. Result: The adsorbent CAF-MAC NCs were investigated under ultrasound-assisted conditions for the removal of the pesticide diazinon from aqueous solutions. The Langmuir adsorption isotherm model indicated that the maximum adsorption of diazinon was 147.05 mg g-1. Conclusion: The new bio-adsorbent offers several significant advantages, including high adsorption capacity, cost-effectiveness, green synthesis, recyclability, and easy separation.

Busbar Design for High-Power SiC Converters

Busbars are critical components that connect high-current and high-voltage subcomponents in high-power converters. This paper reviews the latest busbar design methodologies and offers design recommendations for both laminated and PCB-based busbars. Silicon Carbide (SiC) power devices switch at much higher speeds compared to traditional silicon devices, making them more susceptible to parasitic elements within the busbar. In high-frequency SiC converters, using thicker copper offers limited improvement in high-frequency current handling due to the reduced skin depth at such frequencies. PCB busbars, however, provide several advantages, including reduced loop inductance, enhanced high-frequency current capacity, simplified assembly, and lower costs. Additionally, they enable the integration of components such as sensors, capacitors, and resistors, which can further optimize overall system performance. This paper also presents optimized busbar designs for both module-based and discrete device-based SiC high-power converters, comparing various SiC power module packages and offering design insights. Finally, this paper showcases a 75 kW three-phase inverter utilizing a PCB busbar, demonstrating its potential for achieving high power density and cost-effectiveness in discrete SiC device-based high-power converters.

Fire management now and in the future: Will today's solutions still apply tomorrow?

Climate change and fire management actions are the two key drivers of fire regime changes now and into the future. The predicted effects of these drivers vary between regions and global climate projections; however, it is expected that fire regimes globally are likely to intensify. Increased wildfire extent, frequency and severity mean impacts to people, property, infrastructure, production and the environment are also likely to increase under worsening climate conditions. Fire management programs aim to reduce the influence of worsening climatic conditions on wildfires and risk to assets now and into the future However, given the pace of changes to fire regimes, trade-offs between assets are increasingly likely. Therefore, understanding the cost-effectiveness of fire management in the form of both fuel management and suppression is critical for managers to make informed decisions regarding resource allocation. We develop and test a Bayesian Decision Network (BDN) incorporating data from ~1200 fire regime simulations capturing 16 management strategies across six regions and six climate models. We quantify the effects of management and climate on fire size and risk to environmental, infrastructure, and production assets, as well as people and property. We calculate the overall cost-effectiveness of the management scenario based on the cost of implementing the program and the subsequent cost of impacts caused by wildfires. We found that costs increased under future climate conditions for all management scenarios in most regions. Despite some regional variation in the cost-effectiveness of management scenarios we were able to identify key scenarios which consistently had high cost-effectiveness. These were combinations of prescribed burning and suppression. Importantly, the model clearly demonstrates the risk of a do-nothing approach and highlights that action is needed to prevent high impacts now and into the future and to reduce the overall costs of wildfires.

Barrier materials integrated with gas regulation function are used to reduce the explosion risk of battery systems

Large-capacity lithium iron phosphate batteries are widely used in energy storage stations and electric vehicles due to their high cost-effectiveness and long lifespan. However, research shows that the gases generated during thermal runaway are mainly combustible, which may lead to fires or even explosions. Nevertheless, within the millimeter-scale confined space of a battery pack, effective solutions are scarce, making it challenging to simultaneously suppress kilowatt-level heat transfer and dilute the accumulated combustible gases. This research has developed a multifunctional fire shield with capabilities of “low-temperature heat conduction, medium-temperature heat absorption, high-temperature thermal insulation, and gas regulation.” The material is composed of hydrogel, aluminum hydroxide, ceramic fiber, and ammonium bicarbonate. In the low-temperature range, it exhibits an excellent thermal conductivity (0.58 W·m−1·K−1), capable of maintaining the battery module’s operating temperature within a reasonable range. In the medium to high-temperature range, its thermal conductivity rapidly decreases (0.04 W·m−1·K−1), and with its high phase change enthalpy (1727 J/g), the material can suppress the thermal propagation of a 100 Ah lithium iron phosphate battery module. This material starts to release CO2 at 90℃. In a confined space, the CO2 mixes uniformly with the gases generated during the thermal runaway process of the battery, reducing the risk of these gases (increasing the lower explosion limit by 44 % and reducing the maximum laminar flame speed by 58 %). Therefore, the multifunctional fire shield demonstrates exceptional heat conduction, heat absorption, thermal insulation, and gas regulation performance. This research provides new insights for the safety design of battery systems.

Multimodal Explainability Using Class Activation Maps and Canonical Correlation for MI-EEG Deep Learning Classification

Brain–computer interfaces (BCIs) are essential in advancing medical diagnosis and treatment by providing non-invasive tools to assess neurological states. Among these, motor imagery (MI), in which patients mentally simulate motor tasks without physical movement, has proven to be an effective paradigm for diagnosing and monitoring neurological conditions. Electroencephalography (EEG) is widely used for MI data collection due to its high temporal resolution, cost-effectiveness, and portability. However, EEG signals can be noisy from a number of sources, including physiological artifacts and electromagnetic interference. They can also vary from person to person, which makes it harder to extract features and understand the signals. Additionally, this variability, influenced by genetic and cognitive factors, presents challenges for developing subject-independent solutions. To address these limitations, this paper presents a Multimodal and Explainable Deep Learning (MEDL) approach for MI-EEG classification and physiological interpretability. Our approach involves the following: (i) evaluating different deep learning (DL) models for subject-dependent MI-EEG discrimination; (ii) employing class activation mapping (CAM) to visualize relevant MI-EEG features; and (iii) utilizing a questionnaire–MI performance canonical correlation analysis (QMIP-CCA) to provide multidomain interpretability. On the GIGAScience MI dataset, experiments show that shallow neural networks are good at classifying MI-EEG data, while the CAM-based method finds spatio-frequency patterns. Moreover, the QMIP-CCA framework successfully correlates physiological data with MI-EEG performance, offering an enhanced, interpretable solution for BCIs.

A simplified ratiometric fluorescent sensing strategy for enhanced detection of alkaline phosphatase employing Prussian blue nanozymes and commercially available chromogen

The traditional nanozymes-based ratiometric fluorescence sensing platforms usually necessitate the supplementary addition of fluorescent probes, therefore greatly restricting its convenient and broad application. In this study, a highly sensitive and selective ratiometric fluorescence platform for alkaline phosphatase (ALP) detection was established, only employing Prussian blue (PB) nanozymes and a commercially available chromogen of o-phenylenediamine (OPD). PB nanozymes with remarkable peroxidase-like (POD-like) activity can effectively catalyze OPD chromogen to yield 2,3-diaminophenazine (OPDox) with an intense yellow fluorescence at 573 nm emission peak. Target ALP can facilitate ascorbic acid 2-phosphate (AAP) dephosphorylation to generate phosphate and ascorbic acid (AA). Significantly, both these two resultant hydrolysis products could effectively decrease the OPDox generation via a dual-path based inhibition on the PB nanozymes POD-like activity. On the other hand, the generated dehydroascorbic acid (DHAA) from AA oxidation would exclusively react with OPD chromogen to yield 3-(dihydroxyethyl)furo[3,4-b]quinoxaline-1-one (DFQ) with a strong blue fluorescent signal at 434 nm, which further providing a significant enhancement on the sensing selectivity of ALP detection. As a result, an increased yellow fluorescence of OPDox and decreased blue fluorescence of DFQ could be clearly observed with different ALP addition. A robust linear relationship between the fluorescence ratio of F434/F573 and ALP activity ranging from 0.25 U/L to 6 U/L was obtained, with a low detection limit of 0.112 U/L. This proposed method demonstrates high sensitivity, excellent selectivity, cost-effectiveness, and operational simplicity, yet enabling an effective detection of ALP levels in human serum.

Enhancing the electrochemical performance of Ni-rich cathode through an in-situ La-doping and La4NiLiO8-coating synergistic strategy

Ni-rich layered oxides offer numerous advantages such as high discharge capacity, cost-effectiveness, and environmental friendliness, making them the mainstream cathodes for high-energy density lithium-ion batteries. However, their widespread commercialization is hindered by critical challenges in cycling performance, stemming from unstable interface and structure, particularly at elevated charge cut-off voltages. Herein, a dual-modification strategy combining La doping and La4NiLiO8 coating was proposed to synergistically enhance the surface/interface and structure stability of the LiNi0.83Co0.11Mn0.06O2 (referred to as NCM83) cathode. Unlike conventional methods, a well-designed La(OH)3 coating pretreatment was employed on the Ni0.83Co0.11Mn0.06(OH)2 precursor. This pretreatment aimed to facilitate the in-situ formation of La doping and La4NiLiO8 during the lithiation of the coated precursor, while ensuring desired uniformity of the La4NiLiO8 coating and La doping. This dual-modification strategy integrated the advantages of both La doping and La4NiLiO8 coating, demonstrating excellent capability to improve the overall performance of NCM83. The La doping, acting as a pillar, not only strengthened the layered structure of the cathode to mitigate volume changes, but also enhanced Li+ diffusion by broadening the c axis spacing. The La4NiLiO8 coating efficiently protected the cathode from H2O/CO2 attack and electrolyte erosion, while also facilitating Li+ transport owing to its favorable Li-conductive properties. Such a dual-modification strategy significantly enhanced the electrochemical performance of NCM83, as demonstrated by the excellent long-term cyclic stability, rate capability, and thermal stability at a high charge cut-off voltage of 4.5 V. This work highlighted the benefits of integrating elemental doping and surface coating, while offering a promising synthetic protocol for straightforward and effective dual-modification of various cathodes.

Recovering NDVI over lake surfaces: Initial insights from CYGNSS data enhanced by ERA-5 inputs

The escalating water pollution in many lakes has led to more frequent occurrences of algal bloom disasters in recent decades. The severity of these disasters can be assessed through remote sensing techniques, specifically using the Normalized Difference Vegetation Index (NDVI) for measurement. However, NDVI observations using optical sensors are often affected by cloud and fog in areas with numerous water bodies, such as Taihu Lake. Sensors operating in the microwave band can effectively mitigate this issue, particularly the emerging Global Navigation Satellite System Reflectometry (GNSS-R), which offers high temporal resolution and cost-effectiveness. In this paper, we propose a new method to recover lake-surface NDVI on cloudy days, utilizing GNSS-R observables and auxiliary meteorological data in conjunction with a machine learning regression algorithm called Bagging Tree. We also examine the effective range of GNSS-R data within this application scenario. Meanwhile, the Weighted Linear Regression-Laplacian Prior Regulation Method (WLR-LPRM) image gap-filling algorithm is used as a benchmark to evaluate recovery accuracy. The regression coefficient of NDVI retrieved using the proposed method is 0.95, with a root mean square error (RMSE) of 0.021 and a mean absolute error (MAE) of 0.010. Compared to the previous work on GNSS-R algal bloom detection with overall accuracy of 0.82, this work shows significant improvement in both accuracy and utility. The recovery of lake surface NDVI provides detailed insights into algal blooms, including quantifiable metrics such as the amount and spatial distribution, which are crucial for effective monitoring and management. Additionally, the recovered image textures exhibit high clarity and closely resemble the reference NDVI images. Experimental evaluation using simulated and actual cloud blocks indicates the model’s robustness to recover NDVI under varying cloud cover conditions. In summary, this study demonstrates the capability of GNSS-R aided by supplementary data for recovering missing NDVI values on lake surfaces when optical observations are absent for the first time.

A comparative study on Arc- and vacuum induction-melting for Ti16.6Zr16.6Hf16.6Co10Ni20Cu20 high entropy shape memory Alloy Production

Arc-melting (AM) as a primary method for casting high entropy alloys (HEAs) ensures rapid alloy screening with minimal material input, high cost-effectiveness, and high cooling rates. However, the limitations of AM on a laboratory scale, particularly its constrained sample size and the necessity for remelting steps to ensure homogeneity, hampers thorough mechanical and functional testing of bulk materials. Therefore, this study features a comparative analysis between AM and vacuum induction-melting (VIM) techniques for High Entropy Shape Memory Alloys (HE-SMAs) production, focusing on the senary alloy Ti16.6Zr16.6Hf16.6Co10Ni20Cu20, known for its potential functional applications and high sensitivity to material inhomogeneity. The alloy’s composition, including high-melting point elements like Hf, Ti and Zr, makes it a well-suited candidate for assessing the capabilities of VIM in producing homogeneous bulk materials. The employment of binary pre-alloys in both AM and VIM processes reduced the necessity for remelting steps and ensured better initial quality for subsequent heat treatments. A homogenization treatment at 900 °C for 100 h of an AM-produced senary alloy showed only slight improvements compared to the same alloy produced via VIM, largely due to the slow diffusion of the larger Hf and Zr atoms from the dendrites into the solid solution. This suggests that VIM can achieve comparable levels of homogenization in substantially less time than required for AM-treated samples. The findings finally indicate that by using VIM, when combined with binary pre-alloys, one achieves more homogeneous alloys with reduced heat-treatment time, making it a viable method for HE-SMA production.

Three sample preparation methods for clinical determination of CDK4/6 inhibitors with endocrine therapy in breast cancer patient plasma using LC-MS: Cross-validation (red), ecological (green) and economical (blue) assessment

Cyclin D-dependent kinase 4/6 inhibitors palbociclib, ribociclib and abemaciclib, in combination with aromatase inhibitors anastrozole and letrozole or oestrogen receptor degrader fulvestrant, are being assessed as candidates for therapeutic drug monitoring. An ideal bioanalytical method for their determination in patient plasma samples is therefore of high interest, as there is no routine reference method yet available in the clinical practice. In this work, three sample preparation approaches – dispersive liquid-liquid microextraction (DLLME), solid-phase extraction (SPE), and newly developed phospholipid removal (PLR) for LC-MS determination of these six drugs are comprehensively assessed. The methods are validated in the clinically relevant linear ranges with remarkable precision (RSD ≤6.9 %) and accuracy (bias −13.6 – 11.8 %). To compare the procedures in a real-world setting, they are applied on 38 samples from breast cancer patients. The differences between paired results are below 20 % for more than 92 % of the repeats and the RSD is ≤13.1 % between the corresponding results. Statistical comparison of the results reveals excellent overall agreement between the methods (Lin’s concordance correlation coefficient ≥0.9969, maximal Bland-Altman bias 6.3 %). DLLME proved to be the most ecologically acceptable method due to the high degree of miniaturisation (AGREEprep score 0.44), PLR enabled very high sample throughput and cost-effectiveness (BAGI 72.5), while SPE showed the best analytical performance (redness score 100). All three methods are suitable for their designated purpose, and the choice of the ideal method can be made based on the scope of application, available funds and equipment and desired ecological footprint.

Design of large-spacing, high-stability PANI-NixV2O5 nanobelts as cathode for aqueous zinc-ion batteries using an organic-inorganic co-embedding strategy

Aqueous zinc-ion batteries (AZIBs), distinguished by their high safety and cost-effectiveness, hold significant promise for grid-level energy storage systems. However, the strong interactions between zinc ions and the host lattice of materials lead to suboptimal cycling stability and rate performance. To address this, we present a novel superlattice structure incorporating conductive polymer (PANI) and metal cation (Ni2+) double interlayers, which can be utilized as cathodes for AZIBs. The incorporation of the conductive host polymer polyaniline (PANI) reduces the valence state of vanadium, enhances the electrical conductivity, and effectively expands the channels for zinc ion insertion. Additionally, metal cations (Ni2+) can effectively induce the synergistic interactions with zinc ions, thereby mitigating the electrostatic interactions with the V2O5 host. Consequently, the assembled Zn//PANI-NixV2O5 (PNV) battery exhibits a specific capacity of up to 470 mAh g−1 at 0.1 A g−1, and retains 89.5 % of its capacity after 1000 cycles at 5 A g−1.

Preparation and Electrocatalytic Properties of One-Dimensional Nanorod-Shaped N, S Co-Doped Bimetallic Catalysts of FeCuS-N-C

Metal air batteries have gradually attracted public attention due to their advantages such as high power density, high energy density, high energy conversion efficiency, and clean and green products. Reasonable design of oxygen reduction reaction (ORR) catalysts with high cost-effectiveness, high activity, and high stability is of great significance. Metal organic frameworks (MOFs) have the advantages of large specific surface area, high porosity, and designability, which make them widely used in many fields, especially in catalysis. This paper starts with regulating and optimizing the composition and structure of MOFs. A series of N, S co-doped electrocatalysts FeCuS-N-C were prepared by two high-temperature pyrolysis processes using N-doped carbon hollow nanorods derived from ZIF-8 as the substrate. The one-dimensional nanorod material derived from this MOF exhibits excellent electrocatalytic ORR performance (Eonset = 0.998 V, E1/2 = 0.874 V). When used as the air cathode catalyst for zinc air batteries and assembled into liquid ZABs, the battery discharge curve was calculated and found to have a maximum power density of 142.7 mW cm−2, a specific capacity of 817.1 mAh gZn−1, and a cycling stability test of over 400 h. This study provides an innovative approach for designing and optimizing non-precious metal catalysts for zinc air batteries.