Initial Performance Research Articles

Data-driven designed low Pt loading PtFeCoNiMnGa nano high entropy alloy with high catalytic activity for Zn-air batteries

Developing low Pt loading and high-activity oxygen electrocatalysts is necessary to promote large-scale fuel cell applications. By data-driven and density functional theory calculations, PtFeCoNiMnGa nano high entropy alloy (HEA) was synthesized through liquid-phase reduction and H2 calcination method and loaded on carbon nano-tube (CNT). Due to high entropy, electronic modulation, and cocktail effects, PtFeCoNiMnGa HEA catalyst shows great catalytic activity in oxygen evolution/reduction reaction (OER/ORR). The PtFeCoNiMnGa/CNT showed a low overpotential of 243 mV for OER, and for ORR a mass activity of 1.12 A mgPt−1 (5.3 times than Pt/C). Moreover, the PtFeCoNiMnGa/CNT showed high durability by maintaining 95 % of its initial performance for up to 50 h. In addition, the zinc-air battery assembled with PtFeCoNiMnGa/CNT as the cathode catalyst had an open-circuit potential of 1.52 V and an energy density of 130.6 mW cm−2, and was able to operate stably for 120 h without any significant degradation.

Interfacial engineering of (Ce,Zr)O2 composite barrier layers for enhancing the durability of solid oxide fuel cells

The dense GdxCe1-xO2 (GDC) barrier layer can't prevent the diffusion of small amounts of Sr through grain boundaries. Recent studies indicate that the (Ce,Zr)O2 composite, placed between GDC and YSZ (Y2O3 stabilized ZrO2) electrolyte, can effectively suppress SrZrO3 formation. In this study, we successfully designed and prepared an anode-supported solid oxide fuel cell (SOFC) with a dense GDC/IDL (interdiffusion layer) composite barrier layer, demonstrates excellent inhibition of elemental diffusion through grain boundaries. Compared with the cell with a single dense GDC barrier layer, the cell with GDC/IDL composite barrier layer exhibits largely improved durability, with a two-fold decreased degradation rate of 2.7 %/kh, operated under constant current density of 400 mA/cm2 at 720 °C. The cell with GDC/IDL barrier layer presents decreased initial performances, with lager Rohm of 0.085 Ω cm2 than 0.075 Ω cm2, and smaller initial Pmax of 0.905 W/cm2 than 1.019 W/cm2 at 750 °C. However, the cell performance surpasses the cell with single GDC barrier after 230 h. Thus, we obtain a new strategy to achieve improved cell durability by sacrificing the cell's initial performance. The in-situ constructed continuous and dense GDC/IDL composite optimizes the electrolyte/cathode interface while maintaining decent electrochemical performance, providing a new horizon to design and prepare high performance and durable SOFCs.

Exercise and Acute Cognitive Enhancement.

Substantial research has explored the effects of a single session of exercise on cognitive performance. Meta-analytic reviews provide summary statistics relative to these effects when the cognitive task is performed while exercising and when performed after exercise. When performed concomitantly the effects of exercise are typically small or negative with the exception of moderate effects for speed of cognitive performance during moderate intensity exercise. When cognitive performance is assessed following the cessation of exercise, the effects are typically positive. When considering the effects following exercise, most studies have focused on executive function and episodic memory as the cognitive outcomes of interest with meta-analyses supporting small-to-moderate benefits. When potential moderators related to exercise parameters or participants characteristics are examined, results suggest that 20 minutes of moderate to vigorous intensity exercise consistently benefits performance with larger effects evident for executive function tasks with higher cognitive demands, for long-term memory tasks when exercise is performed prior to information encoding, and when considering individuals with lower initial cognitive performance levels. These observed benefits are likely due to the impacts acute exercise can have on physiological arousal, brain function, and brain neurotrophic factors. Advancements in our understanding of the potential of a single session of exercise to benefit cognitive performance are important for exercise prescription and to inform chronic exercise programs designed to benefit cognition.

Light-weight flexible symmetric supercapacitors based on magnesium-zinc codoped MnO2 nanostructures on carbon cloth as enhanced-performance binder-free electrodes

Developing advanced flexible supercapacitors with high energy and power density is a critical research goal. One promising approach involves combining manganese dioxide (MnO2) with a three-dimensional carbon substrate. This strategy leads to the creation of supercapacitor electrode materials that exhibit high specific capacitance, stability, and environmental friendliness. However, the challenge lies in addressing the low electrical conductivity of MnO2, which limits the rate of electron transfer. The incorporation of metal ions enhances the conductivity of manganese oxide, resulting in a significant increase in lattice defects. These defects create abundant active sites for energy storage. In this study, we developed flexible electrodes based on magnesium-doped MnO2, zinc-doped MnO2, and magnesium-zinc codoped MnO2 nanostructures deposited on carbon cloth (CC) via a one-step hydrothermal method. The supercapacitive behavior of these electrodes was investigated using CV and GCD measurements. The as-prepared electrodes exhibit good electrical conductivity, and their interconnected network structure, composed of sponge-shaped nanostructure layers, results in a large specific surface area. This network also facilitates efficient pathways for effective electron transport. The optimized Mg-Zn codoped MnO2@CC sample exhibits a high capacitance of 79.9 mF/cm² at 0.7 mA/cm² and good rate performance, reaching around 46.59 mF/cm² up to 1.5 mA/cm² in the three-electrode system. Notably, the areal capacitance values calculated from the CV curves for all Mg-Zn codoped MnO2@CC electrodes surpass those of Mg-doped MnO2@CC and Zn-doped MnO2@CC. A flexible symmetric supercapacitor assembled using the best Mg-Zn codoped MnO2@CC as both positive and negative electrodes and PVA-Na2SO4 gel electrolyte delivers a desirable energy density of 0.196 mWh/cm2 at 2.38 mW/cm2. Furthermore, this fabricated supercapacitor demonstrates a broad voltage window of 2.25 V and remarkable cycling stability, retaining 83 % of its initial performance after 4000 charge-discharge cycles.

Characterization of a Covid-fired urban bike delivery system: The Montreal experience

The Covid-19 pandemic led to a rapid change in consumers' demand for home deliveries and traditional delivery modes were unable to deliver consumers and shops due to the sudden increase in demand. This study investigates the results of a local cargo bike delivery initiative in Montreal, Canada, during the forced closure of non-essential businesses from March to May 2020. The objective of this study is to identify the variables which interacted with the performance of a cargo bike delivery initiative while considering the specific context of the Covid-19 pandemic. To assess such changing conditions, a multilevel linear regression analysis is carried out. This analysis allows to quantify the number of home deliveries while considering the impact of the number of active Covid-19 cases and sociodemographic predictors on the cargo bike initiative. The dataset covers 6,700 deliveries made by cargo bikes in the city of Montreal over 16 weeks. The results show that the presence of advertising and the characteristics of consumers' households, such as a smaller household size and a younger average age are amongst the strongest factors for the success of such initiative. The specific context of the Covid-19 pandemic allowed a better penetration of cargo bikes within the last-mile ecosystem.

Development of working memory, processing speed, and psychosocial functions in patients with pediatric cancer.

Many patients after pediatric cancer suffer from long-term cognitive difficulties. This study investigates the development of cognitive and psychosocial functions between diagnosis and one year after cancer treatment and reveals insight into the association between cognitive and psychosocial development and various risk factors. This retrospective clinical record review included fifty-seven patients, aged 4-16 years, that were examined at the beginning of the cancer treatment (T1) and one year after cancer treatment (T2) to evaluate the development of working memory (WM), processing speed (PS), psychosocial functions, and quality of life (QoL). About half of the patients showed stable/favorable cognitive development (PS 51.9%; WM 41.4%). The other half exhibited a non-favorable cognitive development, with a decrease of performance between T1 and T2. In 51.6-77.4%, psychosocial functions remained stable/increased between T1 and T2 and QoL scores remained stable in 42.9-61.9%. Changes in prosocial behavior correlated with the development of PS (r = 0.472, p = 0.010). Age at T1 predicted PS at T2 (p = 0.020) and sex predicted peer relations at T2 (p = 0.046). About half of the patients showed stable/favorable whereas the other half experiencing non-favorable cognitive development. The observed disparities in initial and subsequent cognitive performances highlight the importance of early individualized patient monitoring and interventions. IMPACT: We investigated the cognitive and psychosocial development of pediatric cancer patients between diagnosis and one year after termination of cancer treatment. About half of the patients showed stable or favorable cognitive development in processing speed and working memory. The other half exhibited a non-favorable cognitive development, with decreasing performance. Baseline working memory and processing speed was negatively correlated with the respective change score. Changes in prosocial behavior were positively correlated with the development of processing speed. Early individualized patient monitoring and intervention is of crucial importance after pediatric cancer and its treatment.

Development of a Machine Learning Model for the Classification of Enterobius vermicularis Egg.

Enterobius vermicularis (pinworm) infections are a significant global health issue, affecting children predominantly in environments like schools and daycares. Traditional diagnosis using the scotch tape technique involves examining E. vermicularis eggs under a microscope. This method is time-consuming and depends heavily on the examiner's expertise. To improve this, convolutional neural networks (CNNs) have been used to automate the detection of pinworm eggs from microscopic images. In our study, we enhanced E. vermicularis egg detection using a CNN benchmarked against leading models. We digitized and augmented 40,000 images of E. vermicularis eggs (class 1) and artifacts (class 0) for comprehensive training, using an 80:20 training-validation and a five-fold cross-validation. The proposed CNN model showed limited initial performance but achieved 90.0% accuracy, precision, recall, and F1-score after data augmentation. It also demonstrated improved stability with an ROC-AUC metric increase from 0.77 to 0.97. Despite its smaller file size, our CNN model performed comparably to larger models. Notably, the Xception model achieved 99.0% accuracy, precision, recall, and F1-score. These findings highlight the effectiveness of data augmentation and advanced CNN architectures in improving diagnostic accuracy and efficiency for E. vermicularis infections.

Flexible all-solid-state asymmetric supercapacitor based on Ti3C2Tx MXene/graphene/carbon nanotubes

As the emerging 2D materials, MXenes have been regarded as promising electrode materials in supercapacitor as power supply for wearable and portable electronic devices due to their metallic conductivity, hydrophilic feature, abundant functional groups, and large theoretical specific surface area etc. The capacity and rate capability of MXene-based electrodes is limited by the sheet restacking, hindering their further development. Herein, Ti3C2Tx MXene/graphene/carbon nanotubes (MXene/graphene/CNTs) flexible film was successfully fabricated by vacuum assisted filtration. Intercalation and confinement of graphene and carbon nanotubes between the Ti3C2Tx MXene nanosheets could increase the spacing and specific surface area, alleviate the stacking of MXene nanosheets, thus enhancing the electrochemical properties. The prepared MXene/graphene/CNTs film possesses increased specific surface area, average pore size, pore volume (112.259 m2g−1, 9.982 nm, 0.276 cm3g−1), compared to MXene (93.088 m2g−1, 3.786 nm, 0.088 cm3g−1). Consequently, MXene/graphene/CNTs delivers high area specific capacitance of 1862.5 mF cm−2 at 1 mA cm−2 and excellent rate capability (1525 mF cm−2, 81.88 % at 20 mA cm−2). Flexible asymmetric supercapacitor constructed with MXene/graphene/CNTs film and active carbon as positive and negative electrodes, and PVA/H2SO4 gel as solid electrolyte, achieves an energy density of 133.75 μWh cm−2 at the power density of 750 μW cm−2. In addition, there is still 94.9 % of the initial performance after cycling 8000 charge/discharge cycles at 10 mA cm−2, corresponding to a single-turn decay rate down to 0.00019 %. Besides, this flexible supercapacitor manifests outstanding electrochemical stability at different bending times. These advantages make the flexible supercapacitor device very promising for wearable electronics applications.

Making a benign buried bottom interface for high-performance perovskite photovoltaics with a chelating molecule

Interfacial modifications can significantly enhance the photovoltaic performance of perovskite solar cells (PSCs). We introduced an auxiliary chelating agent, 5,6-isopropyridine-L-ascorbic acid (ILAA), to modify the SnO2/perovskite buried interface. By a non-destructive peeling process using a UV epoxy glue, we exposed the buried bottom of the perovskite layer for direct examination. ILAA not only suppressed SnO2 defects and hydroxyl oxygen but also prevented deterioration of the buried bottom of the perovskite layer. Theoretical calculations also confirmed the greater competitiveness of ILAA in interacting with perovskite molecules than dimethyl sulfoxide. The introduction of ILAA optimized the energy band structure of the SnO2-perovskite interface, therefore enhanced the built-in electric field and facilitated efficient charge transfer. Consequently, ILAA-modified PSCs achieved power conversion efficiencies of 24.04 % and 19.10 % for microdevices (0.09 cm2) and modules (19.32 cm2), respectively. These unencapsulated devices exhibited excellent humidity and thermal stability, maintaining over 80 % of their initial performances after being aged in air for 1000 h or continuously heated for 500 h.

AutoTherm: A Dataset and Benchmark for Thermal Comfort Estimation Indoors and in Vehicles

Thermal comfort inside buildings is a well-studied field where human judgment for thermal comfort is collected and may be used for automatic thermal comfort estimation. However, indoor scenarios are rather static in terms of thermal state changes and, thus, cannot be applied to dynamic conditions, e.g., inside a vehicle. In this work, we present our findings of a gap between building and in-vehicle scenarios regarding thermal comfort estimation. We provide evidence by comparing deep neural classifiers for thermal comfort estimation for indoor and in-vehicle conditions. Further, we introduce a temporal dataset for indoor predictions incorporating 31 input signals and self-labeled user ratings by 18 subjects in a self-built climatic chamber. For in-vehicle scenarios, we acquired a second dataset featuring human judgments from 20 subjects in a BMW 3 Series. Our experimental results indicate superior performance for estimations from time series data over single vector input. Leveraging modern machine learning architectures enables us to recognize human thermal comfort states and estimate future states automatically. We provide details on training a recurrent network-based classifier and perform an initial performance benchmark of the proposed dataset. Ultimately, we compare our collected dataset to publicly available thermal comfort datasets.

Mechanical properties of sisal fiber-reinforced fly ash cement mortar activated by sodium sulfate

The substantial use of fly ash as cement replacement to reduce carbon emissions is identified as the primary goal, 50 % of the cement was substituted with fly ash and in order to avoid the low early stage mechanical properties of the composite, varying doses of sodium sulfate were used as an activator of fly ash. Additionally, sisal fibers were chosen to enhance the flexural strength. The results indicate that sodium sulfate significantly enhances the early stage mechanical performance of the cementitious composites and the matrix exhibits the optimal mechanical performance at 4 wt% sodium sulfate dosage (4SS). The addition of sisal fibers resulted in an enhancement of the flexural performance of the material. The collaboration between sodium sulfate and sisal fibers results in an improvement in the initial flexural performance of the material while preserving its compressive strength. The single-fiber pullout test further confirmed that adding sodium sulfate enhances the fiber-matrix interface and bonding. Microstructural investigations by XRD, TGA, MIP, and SEM-EDS revealed accelerated hydration and early formation of a large amount of ettringite due to the addition of sodium sulfate. Ettringite acts as a gel nucleation seed, promoting the formation of a dense gel structure and effectively improving the early mechanical properties of the composite. Furthermore, with curing age, sodium sulfate also effectively improves the pore structure of the matrix in the late hydration stage.

Sub-national scale mapping of individual olive trees integrating Earth observation and deep learning

The olive tree holds great cultural, environmental, and economic significance in the Mediterranean region. In particular, Morocco has been making dedicated investments over $10 billion since 2008 to fuel the transition from cereal to olive production. Understanding the spatial extent of this large-scale land conversion is critical for a variety of socioeconomic purposes. In response to this demand, we conducted a study to map individual olive trees in northern Morocco using satellite imagery and deep learning techniques at a sub-national scale. This study utilized cloud-free, very-high-resolution DigitalGlobe imagery collected between 2018 and 2022 to identify each individual olive tree in six northern Morocco provinces. We compared various deep learning models, including both transformer-based and CNN-based models, to generate patch-level spatial constraints and pixel-level tree identification. We found that transformer-based models outperformed CNN-based models in both tasks. Additionally, spatially constraining the pixel-level results improved olive tree mapping accuracy to varying degrees, depending on the initial performance of the model. The evaluation of the olive map generated from this study shows high accuracy in both surveyed and unsampled regions. This research represents the first-of-its-kind individual olive tree mapping at the sub-national scale that can help monitor the large-scale land conversions such as about 110,000 ha of olive plantings in the six Moroccan provinces studies here. Meanwhile it demonstrates a cost-effective and efficient prototype approach that can be adapted to identify similar tree crop expansion occurring in other parts of the world.

Basal knowledge in the field of pediatric nephrology and its enhancement following specific training of ChatGPT-4 "omni" and Gemini 1.5 Flash.

We aimed to evaluate the baseline performance and improvement of ChatGPT-4 "omni" (ChatGPT-4o) and Gemini 1.5 Flash (Gemini 1.5) in answering multiple-choice questions related to pediatric nephrology after specific training. Using questions from the "Educational Review" articles published by Pediatric Nephrology between January 2014 and April 2024, the models were tested both before and after specific training with Portable Data Format (PDF) and text (TXT) file formats of the Educational Review articles removing the last page containing the correct answers using a Python script. The number of correct answers was recorded. Before training, ChatGPT-4o correctly answered 75.2% of the 1395 questions, outperforming Gemini 1.5, which answered 64.9% correctly (p < 0.001). After training with PDF files, ChatGPT-4o's accuracy increased to 77.8%, while Gemini 1.5 improved significantly to 84.7% (p < 0.001). Training with TXT files showed similar results, with ChatGPT-4o maintaining 77.8% accuracy and Gemini 1.5 further improving to 87.6% (p < 0.001). The study highlights that while ChatGPT-4o has strong baseline performance, specific training does not significantly enhance its accuracy. Conversely, Gemini 1.5, despite its lower initial performance, shows substantial improvement with training, particularly with TXT files. These findings suggest Gemini 1.5's superior ability to store and retrieve information, making it potentially more effective in clinical applications, albeit with a dependency on additional data for optimal performance.

Carboxylate Pseudo-Halide-Assisted crystallization and antioxidant strategy for stable wide bandgap tin perovskite photovoltaics

The facile oxidation of Sn2+ to Sn4+ and uncontrolled crystal growth are significant challenges in the development of wide-bandgap (WBG) tin perovskite solar cells (TPSCs), affecting their performance and stability. This study introduces three strategically synthesized carboxylate pseudo-halides—3,3-diphenylpropylammonium trifluoroacetate (D-TFA), 3,3-diphenylpropylammonium acetate (D-Ac), and 3,3-diphenylpropylammonium formate (D-Fo), —as additives to mitigate Sn2+/Sn4+ oxidation in WBG TPSCs. These pseudo-halides coordinate with Sn2+, reducing electron density at the carboxylate carbon and forming robust bonds with SnI2(DMSO)3, thus impeding Sn2+ oxidation through oxygen adsorption. Among them, the formate ion (Fo-) in D-Fo exhibited the most significant deshielded peak, indicating the strongest coordination with Sn2+ and superior antioxidation effects by reducing reactivity with ambient oxygen. These interactions, along with 3,3-diphenylpropylammonium (DPA+), aid in controlling crystal growth, enhancing the formation of highly crystalline thin films, and improving overall stability. Consequently, D-Fo-assisted WBG tin perovskites demonstrated strong suppression of phase segregation under prolonged illumination of the air mass 1.5 spectrum, retaining 90 % of their initial characteristics. Ultimately, the D-Fo-enhanced WBG TPSC achieved an exemplary power conversion efficiency of 10.69 % with minimal hysteresis, surpassing the control cell’s 7.43 %. Furthermore, the unencapsulated D-Fo device maintained nearly 87 % of its initial performance after 960 h under ambient conditions.

Achieving ultra-low voltage fading in Co-free Li-rich layered oxides via effortless surface defect engineering

Cobalt (Co)-free lithium (Li)-rich layered oxides (LLOs) have emerged as promising cathode materials for the next generation of Li-ion batteries, attributed to their competitive market positioning and high energy density. Nevertheless, challenges arise from surface oxygen loss due to irreversible anionic redox reactions, leading to severe voltage and capacity decay that hinder the large-scale adoption of LLOs. Herein, we present an innovative, facile, and environmentally friendly hydrothermal approach to induce surface reconstruction of Li1.2Mn0.6Ni0.2O2 material. A multifaceted combination involving the spinel phase, oxygen vacancies, and reduced manganese is orchestrated to alleviate the irreversible oxygen redox and impressively enhance Li-ion diffusion. The modified sample, owing to this surface transition, demonstrates low-strain and low-distortion properties along with a substantial improvement in structural stability, supported by both experimental validations and theoretical studies. As a result, the engineered sample exhibits exceptional capacity retention of 97.12% after 150 cycles at 1C, with an ultra-low voltage decay (0.91 mV cycle−1). Additionally, noteworthy enhancements in initial coulombic efficiency and rate performance are also observed. This straightforward surface defect engineering method offers a pathway to developing “low-strain” LLOs with superior electrochemical performance, thereby laying a solid foundation for future commercial applications.

External Validation of the Charlson Comorbidity Index-based Model for Survival Prediction in Thai Patients Diagnosed with Dementia

BackgroundThe Charlson Comorbidity Index (CCI) is commonly employed for predicting mortality. Nonetheless, its performance has rarely been evaluated in patients with dementia. This study aimed to examine the predictive capability of the CCI-based model for survival prediction in Thai patients diagnosed with dementia.MethodsAn external validation study was conducted using retrospective data from adults with dementia who had visited the outpatient departments at Maharaj Nakorn Chiang Mai Hospital between 2006 and 2012. The data obtained from electronic medical records included age, gender, date of dementia diagnosis and death, types of dementia, and comorbidities at the time of dementia diagnosis. The discriminative ability and calibration of the CCI-based model were estimated using Harrell’s C Discrimination Index and visualized with calibration plot. As the initial performance did not meet satisfaction, model updating and recalibration were performed.ResultsOf 702 patients, 56.9% were female. The mean age at dementia diagnosis was 75.22 (SD 9.75) year-old. During external validation, Harrell's C-statistic of the CCI-based model was 0.58 (95% CI, 0.54–0.61). The model showed poor external calibration. Model updating was subsequently performed. All updated models demonstrated a modest increase in Harrell's C-statistic. Temporal recalibration did not significantly improve the calibration of any of the updated models.ConclusionThe CCI-based model exhibited fair discriminative ability and poor calibration for predicting survival in Thai patients diagnosed with dementia. Despite attempts at model updating, significant improvements were not achieved. Therefore, it is important to consider the incorporation of other influential prognostic factors.

Research on the Prediction of Optimal Frequency for Vibration Mixing and Comparison on Initial Performance of Cold-Recycled Asphalt Emulsion Mixture.

The multicomponent cold-recycled asphalt emulsion mixture (CRAEM) has the ability of antireflection cracking between the base and the bottom surface layer, but it has secondary compaction and residual void, which is not conducive to crack resistance and fatigue performance. The application of high-frequency vibration mixing technology can reduce voids and improve crack resistance, but it is limited by the complexity of testing to determine the optimal mixing frequency. The fractal dimension of gradation is deduced by fractal theory, and the prediction model for optimal frequency is proposed. Dry, wet, freeze-thaw splitting tests, and rutting tests were employed to test the early mechanical properties of high-frequency vibration mixing specimens corresponding to different vibration accelerations, and mercury inclusion tests were utilized to compare the void distribution corresponding to the optimal mixing frequency and forced mixing, and to verify the prediction model for optimal frequency. The results indicate that the high-frequency vibration mixing technology is able to benefit the initial cracking resistance (28.1% increase), moisture stability (11.2% increase), and high-temperature stability on the macro level on the optimal frequency. Meanwhile, the void distribution structure can be optimized, reducing the proportion of harmful voids and increasing the proportion of transitional pores on the micro level. However, the freeze-thaw resistance needs to be further studied. This study reduces the number and cost of experiments to determine the optimal frequency, and provides theoretical guidance and technical support for the engineering application of the CRAEM.

Understanding the Perspectives of Key Stakeholders toward Medicare's Home Health Value-Based Purchasing (HHVBP) in the US

ObjectivesThis study examines the perspectives of key stakeholders in home health toward Medicare's Home Health Value Based Purchasing (HHVBP) program, piloted among home health agencies (HHAs) in 9 states from 2016 to 2021, and based on initial performance, was expanded to the remaining 41 (nonpilot) states in January 2023. DesignWe conducted semistructured interviews wherein we inquired participants’ views toward and experiences with HHVBP. We used convenience and purposive sampling to obtain diversity in HHA size, geography, and quality. Setting and ParticipantsWe conducted interviews from July 2022 to May 2023 with HHA leaders, staff, and clinicians, advocacy and trade organization leaders, and policy experts from pilot and nonpilot states. MethodsWe used thematic analysis to develop a codebook that included framework-derived, a priori, and inductive codes. We identified key themes and subthemes accordingly. ResultsForty-seven stakeholders representing 25 unique organizations participated: 22 (47%) from pilot states and 25 (53%) from nonpilot states; of these, 24 (51%) were HHA leaders, 13 (28%) were organizational leaders, and 10 (21%) were clinicians; 26 (55%) were centered in the Northeast, 9 (19%) in the West, 7 (15%) had a national presence, and 5 (11%) were centered across the South. Four key themes emerged. There were (1) wide variations in awareness, understanding, and attitudes toward HHVBP regardless of pilot status or stakeholder type; (2) concerns about aspects of HHVBP, including consequences for HHAs and patients; (3) a range of strategies used by HHAs to address HHVBP; (4) other concurrent issues that HHAs were navigating alongside HHVBP. Conclusions and ImplicationsDespite HHVBP's national implementation, awareness of and attitudes toward HHVBP varied across stakeholders from pilot and nonpilot states, as did efforts to address it. Although some viewed the policy favorably, others were concerned it could negatively impact HHAs and patients. Understanding the experiences of stakeholders is vital for illuminating the intended and unintended consequences of HHVBP policy.

Novel cocatalyst MnCo2S4 based on Zn3In2S6 for enhanced photocatalytic hydrogen production and ranitidine degradation

Photocatalysis offers a sustainable method for producing hydrogen and degrading harmful substances such as ranitidine. In this research, metal-like properties of MnCo2S4 nanoparticles were synthesized using a glycerol precursor and integrated into Zn3In2S6 floral microsphere via a low-temperature hydrothermal method to function effectively as a cocatalyst. The catalyst not only exhibits excellent hydrogen production efficiency but also demonstrates outstanding performance in degrading ranitidine. Under visible light irradiation, the MnCo2S4/Zn3In2S6 composite material reached a maximum hydrogen production rate of 4471.7 µmol·g−1·h−1 within 6 h, which is 10.6 times higher than that of pure Zn3In2S6. After 24 h of reaction over 4 continuous cycles, the hydrogen evolution activity maintained 94.86 % of its initial performance. Additionally, after 60 min of visible light exposure, the composite achieved a ranitidine degradation rate of 94.7 %, significantly surpassing the performance of pure Zn3In2S6. This exceptional photocatalytic activity is attributed to the synergistic interactions between Zn3In2S6 and the conductive cocatalyst MnCo2S4, which facilitate interfacial charge transfer. A potential reaction mechanism was proposed, supported by a series of experiments and characterization techniques. The formation of a Schottky junction at the MnCo2S4/Zn3In2S6 interface enables rapid electron transfer to the MnCo2S4 nanoparticles, preventing electron backflow and thus promoting effective separation of photo-induced charge carriers. Therefore, this study introduces a novel approach for designing metal-semiconductor photocatalysts for efficient hydrogen production and ranitidine degradation.

Composites electron transport layer of PVA-regulated SnO2 for high-efficiency stable perovskite solar cells

As a new and efficient solar cell, perovskite solar cell has attracted wide attention due to its excellent performance. The electron transport layer in perovskite solar cells has a significant impact on device performance. SnO2 films that can be prepared by solution method have become the first choice for electron transport layer due to their simple process and excellent performance. However, the defects in SnO2 thin films and non-radiative recombination sites at the SnO2/Perovskite interface will lead to potential losses in the performance of photovoltaic devices. Therefore, in order to improve the interface loss and interface characteristics and prepare more efficient perovskite solar cells. In this work, a functional polymer, polyvinyl alcohol (PVA), is introduced to regulate the arrangement of SnO2 nanocrystals. The SnO2-PVA composite electron transport layer not only improves carrier transport, but also further affects the growth of perovskite films. PVA inhibits the agglomeration of tin dioxide particles by adding it to the aqueous solution of tin dioxide. Meanwhile, the oxygen vacancy defects in the SnO2 layer have also improved. Correspondingly, SnO2-PVA-based PSCs can be obtained a maximum efficiency of 23.73 %. Attributed to the strengthened interface binding and the improved perovskite crystallization process, the devices obtain good long-term stability, retaining 90 % of their initial performance after 1000 h operation at their maximum power point under 1 sun illumination.