Browse Articles

Bacterial cooperative weaves sustainable rainbow materials

Internet use and uptake of clinical breast examination among Kenyan women of reproductive age

A dendritic nanocatalyst (Fe3O4@SiO2@DETA-TMD) for eco-friendly synthesis of Pyrano[3,2-c]chromene-3-carbonitriles

Sugarcane vinasse remediation through HA–nCaO within a computational sustainability and green SDG framework

Abstract The disposal of sugarcane vinasse, a highly acidic distillery effluent, presents a serious environmental management challenge. In this study, a novel composite adsorbent was developed by integrating green-synthesized nano-calcium oxide (nCaO) with humic acid (HA) via an aqueous co-precipitation method and ultrasonic-assisted stabilization. Characterization (XRD, FTIR, SEM, BET) confirmed the formation of a mesoporous hybrid with a surface area of 112.6 m² g⁻¹, average pore size of 6.3 nm, and enhanced surface basicity due to HA functionalization. Batch adsorption experiments demonstrated that an optimal dosage of 5 g L⁻¹ achieved 82.4% COD removal, 76.1% TOC reduction, and 89.5% color removal within 90 min, surpassing conventional treatments such as lime neutralization, Fenton oxidation, anaerobic digestion, and biochar systems under comparable conditions. The acidic vinasse (pH < 4) was neutralized to 7.6 ± 0.2 without the addition of external alkali. The incorporation of a chalcogel stage further promoted precipitation of suspended solids and volatile fatty acids, reducing residual organic load. Kinetic modeling indicated a pseudo-second-order fit (R² = 0.991), suggesting a chemisorption-dominated mechanism coupled with acid–base neutralization and partial precipitation. Reusability testing confirmed ≥ 68% efficiency retention after four cycles. Sustainability evaluation using the Need Quality Sustainability (NQS) index and Koel’s Pyramid metrics indicated favorable environmental and operational performance, aligning with UN SDG 6 (Clean Water and Sanitation) and SDG 12 (Responsible Consumption and Production).

Predicting plant stress using SAM-L: novel self-adaptive-meta learner with XAI based on soil moisture and chlorophyll analysis

Neanderthal DNA reveals how human faces form

Double-strand break-free and transgene-free genome editing in the microalga Nannochloropsis oceanica using removable vectors containing the CRISPR base editing system

A digital-intercultural competence model for educational managers: toward sustainable educational leadership in Kazakhstan

The recombinant protein of scorpion venom phospholipase A2 exhibits potential anti-leishmanial activity

Health risk assessment of penconazole fungicide residues in grapes: insights from Monte Carlo simulation

Competing quantum tunneling processes of heavy and light particles in isocyanic acid radical anions

Inter-individual variability in the relationship between propulsion force and walking speed in subacute stroke

Integrated FDM optimization with multivariate capability analysis for dimensional and compressive mechanical properties

PET-MAD as a lightweight universal interatomic potential for advanced materials modeling

Abstract Machine-learning interatomic potentials have greatly extended the reach of atomic-scale simulations, offering the accuracy of first-principles calculations at a fraction of the cost. Leveraging large quantum mechanical databases and expressive architectures, recent universal models deliver qualitative accuracy across the periodic table but are often biased toward low-energy configurations. We introduce PET-MAD, a generally applicable interatomic potential trained on a dataset combining stable inorganic and organic solids, systematically modified to enhance atomic diversity. Using a moderate but thoroughly consistent level of electronic-structure theory, we assess PET-MAD’s accuracy on established benchmarks and advanced simulations of six materials. Despite the small training set and lightweight architecture, PET-MAD is competitive with the state-of-the-art machine-learned interatomic potentials for inorganic solids, while also being reliable for molecules, organic materials, and surfaces. It is stable and fast, enabling the near-quantitative study of thermal and quantum mechanical fluctuations, functional properties, and phase transitions out of the box. It can be efficiently fine-tuned to deliver full quantum mechanical accuracy with a minimal number of targeted calculations.

Inverse design framework for 4D printed structures using the finite element method

Long-term heat stress induces transcriptomic reprogramming of hormone signaling and metabolic pathways during rapeseed flowering

Autocrine ECM molecules establish MSC quiescence during incisor development by disrupting WNT ligand trafficking process

A multi-granularity feature fusion approach with attention for facial expression recognition

A novel zinc finger protein gene signature for prognostic prediction, tumor microenvironment characterization, and therapeutic response in uterine corpus endometrial carcinoma

Light-insensitive organic solar-powered amplifiers

Abstract Wearable bio-sensors using organic electrochemical transistors (OECTs) powered by flexible organic solar cells (OSCs) show promise for electrophysiological monitoring. However, single OECT bio-sensors face unstable outputs due to the limitations of OSCs under low-light conditions and poor energy autonomy. Here, we show a low-power self-powered physiological sensor employing a dual-OECTs configuration, connected in series and powered by the optimized OSCs, which shows more stable signal output and faster response compared to single OECT bio-sensors. Our devices employ the efficient and more stable OSCs to power amplifier by suppressing charge recombination and improving flexibility, thereby facilitating long-term, on-demand use. This integrated device can be attached to human-skin to stably monitor signals, including electrocardiograms, electromyograms and electrooculograms across a wide range of illumination intensities (500 lux-50,000 lux). The design offers a simple architecture for wearable low-power self-powered bio-sensors without external energy supplies/storage, highlighting their potential in real-time disease diagnosis and prevention scenarios.