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Stretchable all-gel organic electrochemical transistors

Enhanced image hash using cellular automata with sponge construction and elliptic curve cryptography for secure image transaction

Anionic Strategy-Modulated Magnetic Ordering in Super-elongated Multiferroic Epitaxial Films

The science behind the lifesum app: an intervention design analysis

Abstract Wellness is an increasingly important part of public health and can prevent both disease and death. Diet and nutrition are important factors that contribute to wellness and predict health outcomes. Adhering to healthy diets is notoriously difficult for many, and some support is often required. Increasingly, that support may be found in the shape of an app in a smartphone. One such app is Lifesum, with some 65 million users worldwide. Lifesum adopts a more holistic approach to nutrition and well-being, and adopted an evidence-based approach to its development. The aim of this study was to describe the scientific, theoretical basis for the Lifesum app and contribute to advancing science in the field of wellness app development. This was an intervention design analysis, designed to describe the theoretical model and intervention theory used to create the Lifesum app in its current embodiment. A pragmatic theoretical model describing behaviour change in the context of healthy eating was devised based on findings in literature. Factors that drive unhealthy eating behaviours, but that were malleable and whose mechanisms of change were feasible to implement, were identified and used to form an intervention theory. The theoretical model and the intervention theory could then guide the implementation of the Lifesum app, illustrated by a logic model. The theoretical model emphasizes personal goal-attainment and motivation as keys to establishing and maintaining healthy eating behaviours, with proximal outcomes being nutrition knowledge, mindfulness about eating and macro-nutrient balance. Nutrition knowledge is achieved through the provision of nutrition information from a vast database on food items, easily available. Continuous feedback on food choices made will enhance this knowledge and a greater awareness of the impact of nutrition on health remains desirable. A more mindful disposition regarding foods is achieved through support in terms of tracking food intake continuously, as well as recommending meals or recipes. After collecting user preferences on health status, biometrics and goals, these meal plans and recipes can be made to offer the optimal macro-nutrient distribution for each individual user. A theoretical model for diet-related behavior change was developed and key dietary issues were identified, outlining mechanisms for positive impact. These insights informed a mechanistic description of the Lifesum app, providing a foundation for future research on intervention outcomes.

Sub-pA dark current infrared photodetection enabled by polarized water-intercalated heterojunctions

Metasomatism of the carbonate rocks of the contact zone with the basalt

Oxetane Cleavage Pathways in the Excited State: Photochemical Kinetic Resolution as an Approach to Enantiopure Oxetanes

Deciphering multi-dimensional interfacial mechanisms via organic cosolvent engineering for sustainable zinc metal batteries

Performance analysis of aquaponics system for longifolia lettuce production in different growth media

Abstract The aim of the present study on the performance of aquaponics systems is to provide a solution to several sustainability issues and obtain organic food free of chemical contaminants. Where, the water quality for rearing Nile tilapia fish is evaluated in three tanks (T1, T2, and T3). In addition, assess the best growth media of the lettuce plant (Lactuca sativa) in boxes of wood, such as the first box was Crushed Stone (CS), the second box was Crushed Gravel + Crushed Stone (CG + CS), and the third box was Crushed Gravel (CG)) for growing lettuce and also used as a filter to improve water quality. The main experimental work of the present study was carried out during the period from September 2022 to April 2023. The experimental system was designed, fabricated, and implemented in the laboratory at the Al Gamaleya Center (31°51′56.7 N and 31°10′44.2 E), Dakahlia Governorate, Egypt. Fish growth parameters were measured in terms of weight and weight gain weekly, and fish growth indicators and water quality indicators of the system were calculated weekly in the three tanks for 17 weeks, and also lettuce crop quality indicators were measured for the three boxes. For water quality indicators, dissolved oxygen (DO), toxic ammonia (NH₃), pH, temperature, total dissolved solids and electrical conductivity were analysed. In addition, lettuce growth parameters were measured in the three boxes with the three different media by leaf number, leaf width, leaf length and root diameter. The effect of fish growth environment (tanks), time, and their interaction as independent variables on water quality indicators were studied. The results indicated that growth rate for fish and lettuce was better in the third tank and third box respectively. However, the current results also showed that the levels recorded in the three tanks for water quality indicators do not exceed the permissible levels required for fish farming and production, but third unit (tank + box) had the best overall result for all parameters under study and where, it was the best in general. The most important results of this analysis were the significant p-values for the indicators NH3 and DO with T, and t. The interaction T*t was not significant for DO and significant for NH3. The R2 values were 0.914 and 0.873 respectively for the growth indicators as follows: NH3 and DO respectively.

Rapid Fluorochromic Sensing of Tertiary Amines and Opioids via Dual-Emissive Ground and Excited Charge-Transfer States

High-quality-factor viscoelastic nanomechanical resonators from moiré superlattices

Dissolving microneedles fabricated from 3D-printed master molds for application in veterinary medicine

Highly Efficient Charge Transfer between Water and Two-Dimensional Materials with Polar Bonds

Porous hierarchically ordered hydrogels demonstrating structurally dependent mechanical properties

Integrative analysis of blood biomarkers and clinical variables improves early detection of aggressive prostate cancer

Abstract Aggressive prostate cancer (PC) represents a significant health concern. Conventional screening methods, primarily based on prostate-specific antigen (PSA) levels, lack specificity, leading to an urgent need for more accurate diagnostic tools. This study investigates whether integrating clinical and routine blood laboratory parameters can improve the early non-invasive prediction of aggressive PC. In a pilot study of 578 patients with suspicion of PC, 28 laboratory values alongside data on family history, diet, and lifestyle were analyzed. A logistic regression classifier was developed, with model performance evaluated using repeated k-fold cross-validation on the complete dataset (n = 282). Participants were categorized into healthy, moderate PC (ISUP 1–2), and aggressive PC (ISUP 3–5). Significant associations were found between PC aggressiveness and lower levels of androstenedione, dehydroepiandrosterone-sulfate (DHEA-S) and free PSA%, as well as higher levels of sex hormone binding globulin (SHBG). The integration of these serum markers with clinical parameters into a new multi-stage risk classifier significantly improved the predictive accuracy for aggressive PC, outperforming PSA-only methods. The integration of DHEA-S, androstenedione, and SHBG as widely available and cost-effective novel blood biomarkers offers a more targeted, non-invasive prediction of aggressive PC. This approach could reduce reliance on invasive prostate biopsies and expensive magnetic resonance imaging.

Total Synthesis of (+)-Hazuntiphylline, (−)-Anhydrohazuntiphyllidine, and (−)-Hazuntiphyllidine

Ion desolvation for boosting the charge storage performance in Ti3C2 MXene electrode

Abstract Clarifying the relationship between ion desolvation, ion-electrode interactions, and charge storage capacity during ion intercalation in host electrode materials is crucial for advancing fast and efficient energy storage systems. However, the absence of direct evidence for ion desolvation and lack of detailed understanding of the interactions between surface terminations and intercalated cations (Li ions)/solvents hinder the exploration of their effects on energy storage mechanisms. In this paper, we study the intercalation of Li ions from a non-aqueous electrolyte in two-dimensional metal carbides Ti3C2 MXenes with different surface chemistries: HF-Ti3C2 (F-, OH- and O-terminated) and MS-Ti3C2 (O- and Cl-terminated) MXenes. We are able to visualize the full ion desolvation and solvents-ions co-intercalation in the interlayers of MS-MXene and HF-MXene, respectively at the atomic scale. The combination of several techniques and characterization tools reveal that the complete ion desolvation in Cl- and O-terminated MS-Ti3C2 MXenes is associated with the formation of a dense solid electrolyte interface layer, resulting in improved charge storage capacity. The O-rich surface terminations of MS-MXenes are found to be responsible for the efficient Li ions storage. These findings shed lights on identifying the critical role of non-electrostatic ion-electrode interactions and ion desolvation in designing high-performance energy storage devices.

Optimizing microfluidic chip for rapid SARS-CoV-2 detection using Taguchi method and artificial neural network PSO

Abstract Microfluidic biosensors offer a promising solution for real-time analysis of coronaviruses with minimal sample volumes. This study optimizes a biochip for the rapid detection of SARS-CoV-2 using the Taguchi orthogonal table L9(34), which comprises nine groups of experiments varying four key parameters: Reynolds number (Re), Damköhler number (Da), Schmidt number (Sc), and the dimensionless position of the reaction surface (X). Signal-to-noise (S/N) ratios and analysis of variance (ANOVA) are employed to determine optimal parameters and assess their impact on binding kinetics and response time of the detection device. These obtained optimal parameters correspond to Re = 4.10-2, Da = 1000, Sc = 105, and X = 1. Additionally, results highlight Da as the most influential factor, accounting for 91%, while X has a minimal effect of 0.3%. Furthermore, an artificial neural network optimization technique, specifically particle swarm optimization (PSO), was utilized to predict biosensor performance. Derived from the Full L81(34) design experiment, the PSO model demonstrates its effectiveness compared to the conventional multi-layer perception (MLP) model, thus underlining its potential in this innovative optimization context.

Designer Catalyst-Enabled Regiodivergent Histone Acetylation

Dual carbon sequestration with photosynthetic living materials

Abstract Natural ecosystems efficiently sequester CO 2 but containing and controlling living systems remains challenging. Here, we engineer a photosynthetic living material for dual CO 2 sequestration that leverages biomass production and insoluble carbonate formation via microbially induced carbonate precipitation (MICP). To achieve this, we immobilize photosynthetic microorganisms within a printable polymeric network. Digital design and fabrication of the living structures ensure sufficient light access and nutrient supply to encapsulated cyanobacteria, enabling long-term culture for over a year. We showcase that photosynthetic living materials are able to sequester 2.2 ± 0.9 mg of CO 2 per gram of hydrogel material over 30 days and 26 ± 7 mg of CO 2 over 400 days. These findings highlight the potential of photosynthetic living materials for scalable, low-maintenance carbon sequestration with applications in carbon-neutral infrastructure and CO 2 mitigation.