Additive interaction of family medical history of diabetes with hypertension on the diagnosis of diabetes among older adults in India: longitudinal ageing study in India.

BACKGROUND: The present study aimed to estimate the additive interaction of family history of diabetes and hypertension on the diagnosis of diabetes among individuals aged 45 years and above in India. The coexistence of these two exposures may act synergistically on the risk of diabetes, leading to adverse health outcomes. METHODS: The study utilized the data from the Longitudinal Ageing Study in India (LASI) Wave 1 (2017-2018). The total sample size for the current study was 58,612 individuals aged 45 years and above. Multivariable logistic regression models were employed to determine the individual and joint effect of a family history of diabetes with hypertension on diabetes. An additive model was applied to assess the interaction effect of the family medical history of diabetes with hypertension on the diagnosis of diabetes by calculating three different measures of additive interaction such as the relative excess risk ratio (RERI), attribution proportion due to interaction (AP), and synergy index (S). RESULTS: The prevalence of diabetes was three times higher among individuals with family history of diabetes (27.8% vs. 9.2%) than those without family history. Individuals with family history of diabetes (AOR: 2.47, CI: 2.11 2.89) had 2.47 times higher odds of having diabetes than those without family history. The prevalence of diabetes was significantly higher among individuals with hypertension and family history of diabetes (46.6%, 95% CI: 39.7-53.6) than those without the coexistence of family history of diabetes and hypertension (9.9%, 95% CI: 9.5-10.4), individuals with hypertension and without a family history of diabetes (22.7%, 95% CI: 21.2-24.2), and individuals with family history of diabetes and without hypertension (16.5%, 95% CI: 14.5-18.7). Moreover, the adjusted odds ratio (AOR) of the joint effect between family medical history of diabetes and hypertension on diabetes was 9.28 (95% CI: 7.51-11.46). In the adjusted model, the RERI, AP, and S for diabetes were 3.5 (95% CI: 1.52-5.47), 37% (0.37; 95% CI: 0.22-0.51), and 1.69 (95% CI: 1.31-2.18) respectively, which indicates that there is a significant positive interaction between family history of diabetes and hypertension on the diagnosis of diabetes. The study findings on interaction effects further demonstrate consistent results for two models of hypertension (self-reported hypertension and hypertensive individuals receiving medication) even after adjustment with potential confounding factors on diabetes (self-reported diabetes and individuals with diabetes receiving medication). CONCLUSIONS: The study findings strongly suggest that the interaction of family history of diabetes with hypertension has a positive and significant effect on the risk of diabetes even after adjustment with potential confounding factors. Furthermore, the findings indicate a synergistic effect, emphasizing the importance of considering both family medical history of diabetes and hypertension when assessing diabetes risk and designing preventive strategies or interventions.

Coupling of a streamlined solid-liquid extraction protocol with LC-ESI-MS/MS for the regular oversight of illegal bromate additive use: An accurate and sensitive determination method in preliminary and bakery products.

A reliable solid-liquid extraction protocol coupled with liquid chromatography-electrospray ionization-tandem mass spectrometry in the negative-ion mode was developed and validated for illegal bromate determination in preliminary and bakery products. Crude and dried-treated samples were directly extracted with acetonitrile-water (4:1, v/v). Bromate was determined using a Phenomenex Synergi™ Polar reversed-phase column and MS/MS under multiple reaction monitoring. The chosen solvent efficiently extracted bromate with all applied extraction-assisting techniques (p > 0.05). Although this assay avoids cleanup procedures, matrix effect of <-11% was achieved. Rapid bromate separation in only 8 min was attained by a reversed-phase column. In both commodities, linearity range, R2, recovery%, repeatability, intermediate precision, LOD and LOQ results were 0.05-100 ng mL-1, >0.9999, 88.6-103%, 2.93-9.80% and 9.64-10.10%, 0.015 µg kg-1 and 0.05 µg kg-1, respectively. Out of 288 tested real samples, 13.9% of violations were observed. This high-sensitivity protocol offers effective oversight and consumer protection.

Explainable artificial intelligence modeling of internal arc in a medium voltage switchgear based on different CFD simulations.

The internal arc represents an unintentional release of electrical energy within the switchgear industry. Manufacturers must address this electro-thermal issue in their switchgears. Over the past decades, various researchers and engineering groups have examined the internal arc pressure rise in switchgears to mitigate damages. The high variability in pressure rise among switchgears due to diverse factors such as design, manufacturing, and electrical parameters results in varying reported pressure increases. This issue motivates the application of artificial intelligence (AI) in interpreting internal arc modeling. The present paper explores the impact of manufacturing parameters such as total duct width (TDW), height (H), and ducts condition (DC), along with environmental parameters like initial pressure (IP) and initial temperature (IT), on the maximum pressure (MP) generated during an internal arc in a medium voltage (MV) switchgear. For this purpose, 54 different computational fluid dynamics (CFD) models were built using the parameters indicated. An extreme gradient boosting (XGBoost) machine learning (ML) model was trained using different CFD models, with MP serving as the target variable for the ML model. The obtained results reveal a variation in the MP of the internal arc under the mentioned parameters, ranging from 17835.45 Pa to 144423.2 Pa. Using SHAP data revealed that IP, TDW, and DC were the most significant factors affecting the pressure increase of the internal arc phenomena.

Enhancing prediction and analysis of UK road traffic accident severity using AI: Integration of machine learning, econometric techniques, and time series forecasting in public health research.

This research project explored into the intricacies of road traffic accidents severity in the UK, employing a potent combination of machine learning algorithms, econometric techniques, and traditional statistical methods to analyse longitudinal historical data. Our robust analysis framework includes descriptive, inferential, bivariate, multivariate methodologies, correlation analysis: Pearson's and Spearman's Rank Correlation Coefficient, multiple logistic regression models, Multicollinearity Assessment, and Model Validation. In addressing heteroscedasticity or autocorrelation in error terms, we've advanced the precision and reliability of our regression analyses using the Generalized Method of Moments (GMM). Additionally, our application of the Vector Autoregressive (VAR) model and the Autoregressive Integrated Moving Average (ARIMA) models have enabled accurate time series forecasting. With this approach, we've achieved superior predictive accuracy and marked by a Mean Absolute Scaled Error (MASE) of 0.800 and a Mean Error (ME) of -73.80 compared to a naive forecast. The project further extends its machine learning application by creating a random forest classifier model with a precision of 73%, a recall of 78%, and an F1-score of 73%. Building on this, we employed the H2O AutoML process to optimize our model selection, resulting in an XGBoost model that exhibits exceptional predictive power as evidenced by an RMSE of 0.1761205782994506 and MAE of 0.0874235576229789. Factor Analysis was leveraged to identify underlying variables or factors that explain the pattern of correlations within a set of observed variables. Scoring history, a tool to observe the model's performance throughout the training process was incorporated to ensure the highest possible performance of our machine learning models. We also incorporated Explainable AI (XAI) techniques, utilizing the SHAP (Shapley Additive Explanations) model to comprehend the contributing factors to accident severity. Features such as Driver_Home_Area_Type, Longitude, Driver_IMD_Decile, Road_Type, Casualty_Home_Area_Type, and Casualty_IMD_Decile were identified as significant influencers. Our research contributes to the nuanced understanding of traffic accident severity and demonstrates the potential of advanced statistical, econometric, machine learning techniques in informing evidence based interventions and policies for enhancing road safety.

Evaluating the effects of benzoic acid on nursery and finishing pig growth performance.

Three studies were conducted evaluating the use of benzoic acid in swine diets. In experiment 1, 350 weanling barrows (DNA 200 × 400; initially 5.9 ±â€…0.04 kg) were allotted to one of the five dietary treatments with 14 pens per treatment. Diets were fed in three phases: phase 1 from weaning to day 10, phase 2 from days 10 to 18, and phase 3 from days 18 to 38. Treatment 1 contained no benzoic acid throughout all three phases (weaning to day 42). Treatment 2 included 0.50% benzoic acid throughout all three phases. Treatment 3 contained 0.50% benzoic acid in phases 1 and 2, and 0.25% benzoic acid in phase 3. Treatment 4 contained 0.50% benzoic acid in phases 1 and 2, and no benzoic acid in phase 3. Treatment 5 contained 0.50% benzoic acid in phase 1, 0.25% benzoic acid in phase 2, and no benzoic acid in phase 3. For the overall period, pigs fed 0.50% in the first two phases and 0.25% benzoic acid in the final phase had greater (P < 0.05) average daily gain (average daily gain) than pigs fed no benzoic acid through all three phases, or pigs fed 0.50% in the first two phases and no benzoic acid in the final phase, with pigs fed the other treatments intermediate. Pigs fed 0.50% in the first two phases and 0.25% benzoic acid in the final phase had improved (P < 0.05) gain-to-feed ratio (G:F) compared with pigs fed no benzoic acid throughout all three phases, pigs fed 0.50% in the first two phases and no benzoic acid in the third phase, or pigs fed 0.50%, 0.25%, and no benzoic acid, respectively. For experiment 2, a 101-d trial was conducted using two groups of 1,053 finishing pigs (2,106 total pigs; PIC 337 × 1,050; initially 33.3 ±â€…1.9 kg). Dietary treatments were corn-soybean meal-dried distillers grains with solubles-based with the addition of none, 0.25%, or 0.50% benzoic acid. Overall, pigs fed increasing benzoic acid had a tendency for increased average daily feed intake (linear, P = 0.083) but decreased G:F (linear, P < 0.05). In experiment 3, 2,162 finishing pigs (DNA 600 × PIC 1050; initially 31.4 ±â€…2.2 kg) were used in a 109-d trial. Dietary treatments were formulated with or without 0.25% benzoic acid. For the overall experimental period, pigs fed benzoic acid had increased (P < 0.05) G:F. In summary, feeding benzoic acid elicits improved growth performance when fed throughout the entire nursery period while improved G:F in growing-finishing pigs was observed in one experiment, but not in the other.

Pharmaceutical applications and requirements of resins for printing by digital light processing (DLP).

The digital light processing (DLP) printer has proven to be effective in biomedical and pharmaceutical applications, as its printing method does not induce shear and a strong temperature on the resin. In addition, the DLP printer has good resolution and print quality, which makes it possible to print complex structures with a customized shape, being used for various purposes ranging from jewelry application to biomedical and pharmaceutical areas. The big disadvantage of DLP is the lack of a biocompatible and non-toxic resin on the market. To overcome this limitation, an ideal resin for biomedical and pharmaceutical use is needed. The resin must have appropriate properties, so that the desired format is printed when with a determined wavelength is applied. Thus, the aim of this work is to bring the basic characteristics of the resins used by this printing method and the minimum requirements to start printing by DLP for pharmaceutical and biomedical applications. The DLP method has proven to be effective in obtaining pharmaceutical devices such as drug delivery systems. Furthermore, this technology allows the printing of devices of ideal size, shape and dosage, providing the patient with personalized treatment.

Design of dissimilar material joint for defect-free multi-material additive manufacturing via laser-directed energy deposition.

Additive manufacturing technology has advanced beyond creating optimized features, from strengthening materials to make them lightweight to fabricating multi-material combinations that offer functionalities beyond the capabilities of individual materials. In this study, a lamination method for laser-directed energy deposition (LDED) is developed to achieve dense multi-material features, and a design that combines different and dissimilar materials is developed. To evaluate these novel developments, two materials-AISI 316L stainless steel and Inconel 625-are introduced. Tensile specimens, fabricated via multi-material additive manufacturing using LDED, are subjected to tensile tests that are recorded on video for digital image correlation. After the tests, fracture surface analyses of the fractured specimens are performed via scanning electron microscopy, and optical monitoring analyses are performed on the specimens that are not subjected to the tensile tests. The results indicate that the specimens demonstrate varied mechanical properties due to the influence of lamination direction and order, which affect the formation of critical cracks and pores.

Paediatric clinical study of 3D printed personalised medicines for rare metabolic disorders.

Rare diseases are infrequent, but together they affect up to 6-10 % of the world's population, mainly children. Patients require precise doses and strict adherence to avoid metabolic or cardiac failure in some cases, which cannot be addressed in a reliable way using pharmaceutical compounding. 3D printing (3DP) is a disruptive technology that allows the real-time personalization of the dose and the modulation of the dosage form to adapt the medicine to the therapeutic needs of each patient. 3D printed chewable medicines containing amino acids (citrulline, isoleucine, valine, and isoleucine and valine combinations) were prepared in a hospital setting, and the efficacy and acceptability were evaluated in comparison to conventional compounded medicines in six children. The inclusion of new flavours (lemon, vanilla and peach) to obtain more information on patient preferences and the implementation of a mobile app to obtain patient feedback in real-time was also used. The 3D printed medicines controlled amino acid levels within target levels as well as the conventional medicines. The deviation of citrulline levels was narrower and closer within the target concentration with the chewable formulations. According to participants' responses, the chewable formulations were well accepted and can improve adherence and quality of life. For the first time, 3DP enabled two actives to be combined in the same formulation, reducing the number of administrations. This study demonstrated the benefits of preparing 3D printed personalized treatments for children diagnosed with rare metabolic disorders using a novel technology in real clinical practice.

Associations between static and dynamic changes of platelet counts and in-hospital mortality in critical patients with acute heart failure.

To investigate the predictive value of baseline platelet count and its short-term dynamic changes in the prognosis of patients with acute heart failure (AHF) in the intensive care unit. Patients diagnosed with AHF in the medical information mart for intensive care III and their clinical data were retrospectively filtered. Patients were divided into survivor and non-survivor groups based on their prognosis during hospitalization, and differences in baseline data between groups were compared. Logistic regression models and restricted cubic spline (RCS) plots were performed to evaluate the relationship between baseline platelet counts and in-hospital mortality. Changes and trends in platelet counts were compared between the survivor and non-survivor groups after adjusting for confounders with the generalized additive mixing model (GAMM). A total of 2930 critical patients with acute heart failure were included, of which 2720 were survivors and 210 were non-survivors. Multiple logistic regression models revealed that baseline platelet count was an independent factor in hospital mortality (OR 0.997, 95% CI 0.994-0.999, P-value = 0.018). The RCS plot demonstrated a U-shaped dose-response relationship between baseline platelet count and in-hospital mortality. GAMM analysis suggested that the platelet counts decreased and then increased in the survivor group and gradually decreased in the non-survivor group, with a gradual increase of difference between two groups. After adjusting for confounders, the mean daily increase was -6.014 (95% CI -7.076-4.953, P-value < 0.001). Baseline platelet demonstrated a U-shaped dose-response relationship with adverse outcomes in critical patients with AHF. Early elevation of platelet was correlated with higher in-hospital mortality, indicating that tracking early changes in platelet might help determine the short-term prognosis of critical patients with AHF.

Associations of short-term ambient temperature exposure with lung function in middle-aged and elderly people: A longitudinal study in China.

The short-term associations of ambient temperature exposure with lung function in middle-aged and elderly Chinese remain obscure. The study included 19,128 participants from the Dongfeng-Tongji cohort's first (2013) and second (2018) follow-ups. The lung function for each subject was determined between April and December 2013 and re-assessed in 2018, with three parameters (forced vital capacity [FVC], forced expiratory volume in 1 s [FEV1], and peak expiratory flow [PEF]) selected. The China Meteorological Data Sharing Service Center provided temperature data during the study period. In the two follow-ups, a total of 25,511 records (average age: first, 64.57; second, 65.80) were evaluated, including 10,604 males (41.57%). The inversely J-shaped associations between moving average temperatures (lag01-lag07) and FVC, FEV1, and PEF were observed, and the optimum temperatures at lag04 were 16.5 °C, 18.7 °C, and 16.2 °C, respectively. At lag04, every 1 °C increase in temperature was associated with 14.07 mL, 9.78 mL, and 62.72 mL/s increase in FVC, FEV1, and PEF in the low-temperature zone (

Non-parametric quantile regression-based modelling of additive effects to solar irradiation in Southern Africa.

Modelling of solar irradiation is paramount to renewable energy management. This warrants the inclusion of additive effects to predict solar irradiation. Modelling of additive effects to solar irradiation can improve the forecasting accuracy of prediction frameworks. To help develop the frameworks, this current study modelled the additive effects using non-parametric quantile regression (QR). The approach applies quantile splines to approximate non-parametric components when finding the best relationships between covariates and the response variable. However, some additive effects are perceived as linear. Thus, the study included the partial linearly additive quantile regression model (PLAQR) in the quest to find how best the additive effects can be modelled. As a result, a comparative investigation on the forecasting performances of the PLAQR, an additive quantile regression (AQR) model and the new quantile generalised additive model (QGAM) using out-of-sample and probabilistic forecasting metric evaluations was done. Forecasted density plots, Murphy diagrams and results from the Diebold-Mariano (DM) hypothesis test were also analysed. The density plot, the curves on the Murphy diagram and most metric scores computed for the QGAM were slightly better than for the PLAQR and AQR models. That is, even though the DM test indicates that the PLAQR and AQR models are less accurate than the QGAM, we could not conclude an outright greater forecasting performance of the QGAM than the PLAQR or AQR models. However, in situations of probabilistic forecasting metric preferences, each model can be prioritised to be applied to the metric where it performed slightly the best. The three models performed differently in different locations, but the location was not a significant factor in their performances. In contrast, forecasting horizon and sample size influenced model performance differently in the three additive models. The performance variations also depended on the metric being evaluated. Therefore, the study has established the best forecasting horizons and sample sizes for the different metrics. It was finally concluded that a 20% forecasting horizon and a minimum sample size of 10000 data points are ideal when modelling additive effects of solar irradiation using non-parametric QR.

Prediction models for postoperative recurrence of non-lactating mastitis based on machine learning.

OBJECTIVES: This study aims to build a machine learning (ML) model to predict the recurrence probability for postoperative non-lactating mastitis (NLM) by Random Forest (RF) and XGBoost algorithms. It can provide the ability to identify the risk of NLM recurrence and guidance in clinical treatment plan. METHODS: This study was conducted on inpatients who were admitted to the Mammary Department of Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine between July 2019 to December 2021. Inpatient data follow-up has been completed until December 2022. Ten features were selected in this study to build the ML model: age, body mass index (BMI), number of abortions, presence of inverted nipples, extent of breast mass, white blood cell count (WBC), neutrophil to lymphocyte ratio (NLR), albumin-globulin ratio (AGR) and triglyceride (TG) and presence of intraoperative discharge. We used two ML approaches (RF and XGBoost) to build models and predict the NLM recurrence risk of female patients. Totally 258 patients were randomly divided into a training set and a test set according to a 75%-25% proportion. The model performance was evaluated based on Accuracy, Precision, Recall, F1-score and AUC. The Shapley Additive Explanations (SHAP) method was used to interpret the model. RESULTS: There were 48 (18.6%) NLM patients who experienced recurrence during the follow-up period. Ten features were selected in this study to build the ML model. For the RF model, BMI is the most important influence factor and for the XGBoost model is intraoperative discharge. The results of tenfold cross-validation suggest that both the RF model and the XGBoost model have good predictive performance, but the XGBoost model has a better performance than the RF model in our study. The trends of SHAP values of all features in our models are consistent with the trends of these features' clinical presentation. The inclusion of these ten features in the model is necessary to build practical prediction models for recurrence. CONCLUSIONS: The results of tenfold cross-validation and SHAP values suggest that the models have predictive ability. The trend of SHAP value provides auxiliary validation in our models and makes it have more clinical significance.

Surface Engineering of Escherichia coli to Display Its Phytase (AppA) and Functional Analysis of Enzyme Activities.

Escherichia coli phytase (AppA) is widely used as an exogenous enzyme in monogastric animal feed mainly because of its ability to degrade phytic acid or its salt (phytate), a natural source of phosphorus. Currently, successful recombinant production of soluble AppA has been achieved by gene overexpression using both bacterial and yeast systems. However, some methods for the biomembrane immobilization of phytases (including AppA), such as surface display on yeast cells and bacterial spores, have been investigated to avoid expensive enzyme purification processes. This study explored a homologous protein production approach for displaying AppA on the cell surface of E. coli by engineering its outer membrane (OM) for extracellular expression. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis analysis of total bacterial lysates and immunofluorescence microscopy of non-permeabilized cells revealed protein expression, whereas activity assays using whole cells or OM fractions indicated functional enzyme display, as evidenced by consistent hydrolytic rates on typical substrates (i.e., p-nitrophenyl phosphate and phytic acid). Furthermore, the in vitro results obtained using a simple method to simulate the gastrointestinal tract of poultry suggest that the whole-cell biocatalyst has potential as a feed additive. Overall, our findings support the notion that biomembrane-immobilized enzymes are reliable for the hydrolysis of poorly digestible substrates relevant to animal nutrition.

Dimensioning of Biomimetic Beams under Bending for Additively Manufactured Structural Components.

Additively manufactured mechanical components show great lightweight characteristics and can often be enhanced by integrating biomimetic geometrical features. This study focuses on one specific subcase, namely the substitution of solid cylindrical beams that are under bending with geometrically more complex biomimetic beams. Based on the pseudo-stem of the banana plant as a role model, six geometric beam designs were derived. Given the manufacturing constraints of the PBF-LB/M process, two abstractions were selected for detailed investigation in the main part of this study. The beam lengths were set to 100 mm. Based on parametric optimization simulations, optimal design parameters were identified for the two biomimetic abstractions for 26 different bending load cases ranging from 14 to 350 Nm. Analogous parameter optimizations were performed for a solid cylindrical beam design, which was used as a reference. The results provide detailed design solutions within the investigated intervals for biomimetic beams that can be substituted into more complex mechanical component designs with ease. The analysis provides information on which structures to use for the investigated loads. With the help of the developed numerical models, designers can easily generate biomimetic beam designs for specific bending load values.

Effects of Shrimp Shell-Derived Chitosan on Growth, Immunity, Intestinal Morphology, and Gene Expression of Nile Tilapia (Oreochromis niloticus) Reared in a Biofloc System.

Chitosan (CH) shows great potential as an immunostimulatory feed additive in aquaculture. This study evaluates the effects of varying dietary CH levels on the growth, immunity, intestinal morphology, and antioxidant status of Nile tilapia (Oreochromis niloticus) reared in a biofloc system. Tilapia fingerlings (mean weight 13.54 ± 0.05 g) were fed diets supplemented with 0 (CH0), 5 (CH5), 10 (CH10), 20 (CH20), and 40 (CH40) mL·kg-1 of CH for 8 weeks. Parameters were assessed after 4 and 8 weeks. Their final weight was not affected by CH supplementation, but CH at 10 mL·kg-1 significantly improved weight gain (WG) and specific growth rate (SGR) compared to the control (p < 0.05) at 8 weeks. Skin mucus lysozyme and peroxidase activities were lower in the chitosan-treated groups at weeks 4 and 8. Intestinal villi length and width were enhanced by 10 and 20 mL·kg-1 CH compared to the control. However, 40 mL·kg-1 CH caused detrimental impacts on the villi and muscular layer. CH supplementation, especially 5-10 mL·kg-1, increased liver and intestinal expressions of interleukin 1 (IL-1), interleukin 8 (IL-8), LPS-binding protein (LBP), glutathione reductase (GSR), glutathione peroxidase (GPX), and glutathione S-transferase (GST-α) compared to the control group. Overall, dietary CH at 10 mL·kg-1 can effectively promote growth, intestinal morphology, innate immunity, and antioxidant capacity in Nile tilapia fingerlings reared in biofloc systems.

Additive Effects of Monetary Loss and Positive Emotion in the Human Brain.

In many real-life scenarios, our decisions could lead to multiple outcomes that conflict with value. Hence, an appropriate neural representation of the net experienced value of conflicting outcomes, which play a crucial role in guiding future decisions, is critical for adaptive behavior. As some recent functional neuroimaging work has primarily focused on the concurrent processing of monetary gains and aversive information, very little is known regarding the integration of conflicting value signals involving monetary losses and appetitive information in the human brain. To address this critical gap, we conducted a functional MRI study involving healthy human male participants to examine the nature of integrating positive emotion and monetary losses. We employed a novel experimental design where the valence (positive or neutral) of an emotional stimulus indicated the type of outcome (loss or no loss) in a choice task. Specifically, we probed two plausible integration patterns while processing conflicting value signals involving positive emotion and monetary losses: interactive versus additive. We found overlapping main effects of positive (vs neutral) emotion and loss (vs no loss) in multiple brain regions, including the ventromedial prefrontal cortex, striatum, and amygdala, notably with a lack of evidence for interaction. Thus, our findings revealed the additive integration pattern of monetary loss and positive emotion outcomes, suggesting that the experienced value of the monetary loss was not modulated by the valence of the image signaling those outcomes. These findings contribute to our limited understanding of the nature of integrating conflicting outcomes in the healthy human brain with potential clinical relevance.

Unraveling the Role of 2D Ti3C2Tx MXene Nanosheets in Cu-Based Double Perovskite Active Layer for Enhanced Photovoltaic Performance.

Although the atmospheric stability of lead-free inorganic double perovskite (DP) solar cells (PSCs) looks promising, their further development is hampered by inadequate film quality and non-radiative carrier recombination at the interfaces. Herein, the incorporation of a newly developed intriguing class of 2D material Ti3C2Tx MXene nanosheets with the photo-absorbing Cu2AgBiI6 (CABI) active layer of a fully inorganic solar cell is reported. The highly conductive Ti3C2Tx nanosheets work as a multi-functional additive by tuning the band gap, reducing the non-radiative carrier recombination, and inhibiting carrier accumulation. In addition, the presence of Ti3C2Tx MXene increases the surface free energy of the perovskite film, which elevates the energy barrier for nucleation and realizes a highly crystalline CABI perovskite film. Primarily, the MXene modification accelerates the charge extraction and transport at the interfaces of the active layer, utilizing energy level alignment with the charge transport layers. Consequently, the photo-conversion efficiency (PCE) of the device with MXene is substantially enhanced to 1.50%. Moreover, the 2D Ti3C2Tx nanosheets increased the long-term stability of the devices by retaining 70% of the initial PCE after 1680 h. With regard to relieving the severe carrier recombination at the interfaces, this work sets a new paradigm toward imminent solar energy conversion.

Empowering powdered activated carbon (PAC) with 3D printing: Achieving highly efficient and reusable cationic dye removal.

Powdered activated carbon (PAC) has been extensively used as an effective adsorbent. Despite its excellent adsorption ability, PAC has drawbacks, including difficulty in filtration and reactivation after use, limitations of mass transfer in deeper areas because of its aggregated powder form, and limited applicability in high-flow systems. To overcome these limitations, we used a three-dimensional (3D) printing system to fabricate PAC into a 3D structure. Spectral and microscopic analyses indicated that PAC was embedded into 3D monolith and exhibited high porosity suitable for facile mass transfer. The designed 3D PAC filter effectively removed 200 ppm-methylene blue (MB) within 8 h and showed an adsorption efficiency of 93.4 ± 0.9%. The adsorption of MB onto the 3D PAC filter was described by the pseudo-first-order kinetic and Freundlich isotherm models. The negatively charged 3D PAC filter might attract the positively charged MB, thus favoring the physical adsorption of MB onto the 3D PAC filter. The adsorption performance of the 3D PAC filter was tested at various pH levels of 4-10 and against MB spiked in seawaters and freshwaters to evaluate its feasibility for use in real environments. Finally, the reproducibility and reusability of the 3D PAC filter were demonstrated through repeated adsorption and desorption processes against MB.

LiF/Li3N-Rich Electrode-Electrolyte Interfaces Enabled by Multi-Functional Electrolyte Additive to Achieve High-Performance Li/LiNi0.8Co0.1Mn0.1O2 Batteries.

LiPF6-based carbonate electrolytes have been extensively employed in commercial Li-ion batteries, but they face numerous interfacial stability challenges while applicating in high-energy-density lithium-metal batteries (LMBs). Herein, this work proposes N-succinimidyl trifluoroacetate (NST) as a multifunctional electrolyte additive to address these challenges. NST additive could optimize Li+ solvation structure and eliminate HF/H2O in the electrolyte, and preferentially be decomposed on the Ni-rich cathode (LiNi0.8Co0.1Mn0.1O2, NCM811) to generate LiF/Li3N-rich cathode-electrolyte interphase (CEI) with high conductivity. The synergistic effect reduces the electrolyte decomposition and inhibits the transition metal (TM) dissolution. Meanwhile, NST promotes the creation of solid electrolyte interphase (SEI) rich in inorganics on the Li metal anode (LMA), which restrains the growth of Li dendrites, minimizes parasitic reactions, and fosters rapid Li+ transport. As a result, compared with the reference, the Li/LiNi0.8Co0.1Mn0.1O2 cell with 1.0 wt.% NST exhibits higher capacity retention after 200 cycles at 1C (86.4% vs. 64.8%) and better rate performance, even at 9C. In the presence of NST, the Li/Li symmetrical cell shows a super-stable cyclic performance beyond 500 h at 0.5 mA cm-2/0.5 mAh cm-2. These unique features of NST are a promising solution for addressing the interfacial deterioration issue of high-capacity Ni-rich cathodes paired with LMA.

Meniscal repair with additive manufacture of bioresorbable polymer: From physicochemical characterization to implantation of 3D printed poly (L-co-D, L lactide-co-trimethylene carbonate) with autologous stem cells in rabbits.

Three-dimensional (3D) structures are actually the state-of-the-art technique to create porous scaffolds for tissue engineering. Since regeneration in cartilage tissue is limited due to intrinsic cellular properties this study aims to develop and characterize three-dimensional porous scaffolds of poly (L-co-D, L lactide-co-trimethylene carbonate), PLDLA-TMC, obtained by 3D fiber deposition technique. The PLDLA-TMC terpolymer scaffolds (70:30), were obtained and characterized by scanning electron microscopy, gel permeation chromatography, differential scanning calorimetry, thermal gravimetric analysis, compression mechanical testing and study on in vitro degradation, which showed its amorphous characteristics, cylindrical geometry, and interconnected pores. The in vitro degradation study showed significant loss of mechanical properties compatible with a decrease in molar mass, accompanied by changes in morphology. The histocompatibility association of mesenchymal stem cells from rabbit's bone marrow, and PLDLA-TMC scaffolds, were evaluated in the meniscus regeneration, proving the potential of cell culture at in vivo tissue regeneration. Nine New Zealand rabbits underwent total medial meniscectomy, yielding three treatments: implantation of the seeded PLDLA-TMC scaffold, implantation of the unseeded PLDLA-TMC and negative control (defect without any implant). After 24 weeks, the results revealed the presence of fibrocartilage in the animals treated with polymer. However, the regeneration obtained with the seeded PLDLA-TMC scaffolds with mesenchymal stem cells had become intimal to mature fibrocartilaginous tissue of normal meniscus both macroscopically and histologically. This study demonstrated the effectiveness of the PLDLA-TMC scaffold in meniscus regeneration and the potential of mesenchymal stem cells in tissue engineering, without the use of growth factors. It is concluded that bioresorbable polymers represent a promising alternative for tissue regeneration.