Breast cancer progression is strongly influenced by the physicochemical properties of the surrounding extracellular matrix (ECM). To model the breast ECM, adipose tissue decellularized extracellular matrix (dECM)-derived hydrogels were generated, which preserve a broad spectrum of ECM associated proteins and exhibit stiffness and viscoelastic properties within the physiological range of breast tissue. Proteomic analyses confirmed the biochemical complexity of these materials, while rheological measurements established their mechanically relevant characteristics. Within these 3D dECM hydrogels, the poorly metastatic MCF-7 and highly invasive MDA-MB-231 cell lines displayed their expected lineage specific behaviors. Moreover, these cells exhibited significantly distinct proliferation, invasion and growth patterns in hydrogels with different stiffness and viscoelasticity, highlighting the importance of biophysical cues in modulating cell phenotype. These findings illustrate that user-friendly adipose tissue dECM hydrogels provide a biologically meaningful platform for investigating how microenvironmental mechanics influence cell behavior.
The emergence of the transformer architecture has ushered in a new era of possibilities, showcasing remarkable capabilities in generative tasks exemplified by models like GPT4o, Claude 3, and Llama 3. However, these advancements come with a caveat: predominantly trained on data gleaned from social media platforms, these systems inadvertently perpetuate societal biases and toxicity. Recognizing the paramount importance of AI Safety and Alignment, our study embarks on a thorough exploration through a comprehensive literature review focused on toxic language. Delving into various definitions, detection methodologies, and mitigation strategies, we aim to shed light on the complexities of this issue. Our study primarily focuses on transformer-based architectures, with deep learning methods serving as a crucial baseline for comparison in our exploration of toxic language detection and mitigation. Through our investigation, we uncover a multitude of challenges inherent in toxicity mitigation and detection models. These challenges range from inherent biases and generalization issues to the necessity for standardized definitions of toxic language and the quality assurance of dataset annotations. Furthermore, we emphasize the significance of transparent annotation processes, resolution of annotation disagreements, and the enhancement of Large Language Models (LLMs) robustness. Additionally, we advocate for the creation of standardized benchmarks to gauge the effectiveness of toxicity mitigation and detection methods. Addressing these challenges is not just imperative, but pivotal in advancing the development of safer and more ethically aligned AI systems.
The crystallization of soluble salts poses a significant challenge to mural painting conservation. While cellulose poultices are widely used to desalinate mural paintings due to their high absorption and ease of handling, their effectiveness within the porous network of wall paintings remains a complex issue. For the first time, this study explores the potential of micro-structured cellulose-based foams as an alternative to conventional poultices for desalinating fresco wall paintings. A laboratory experiment compared the efficacy of foams and poultices, using fresco wall painting mock-ups (produced with the Roman technique) that were vacuum-impregnated with salt solutions (chlorides, sulfates, and mixtures). Short and long application times were considered, and foam reusability across multiple application cycles was assessed. Micro-energy dispersive X-ray fluorescence (& micro;-EDXRF) imaging was employed to quantitatively evaluate salt content reduction, both superficially and throughout the mock-up stratigraphy. Results show that foams are considerably more effective than poultices, achieving a salt removal efficiency between 6 and 10 times higher. The uniform micro-porous foam network enables faster desalination, reducing treatment risks and minimizing waste while supporting circular economy principles. This study also demonstrates the utility of & micro;-EDXRF imaging in monitoring desalination efficacy for both surface and cross-section analyses when assessing new desalination protocols.
This study proposes a planar swirl micro-combustor with dual tangential inlets, distinguished by its novel disktype structure-a key departure from traditional cylindrical micro-combustors-to enhance combustion stability and thermal efficiency in micro-scale energy systems. The combustor features a compact circular chamber (10 mm diameter, 0.5 mm height), where hydrogen/air mixtures generate a swirling flow, improving reactant mixing, extending residence time, and promoting heat recirculation. This 2D disk-type design offers superior adaptability for portable power and micro-thermophotovoltaic applications, boasting better prospects than traditional 3D cylindrical counterparts. Naturally, flames exhibit an ellipsoidal morphology, and this inherent structure becomes unstable when the transition from 3D to 2D combustion hinders the formation of the natural flame structure-particularly the recirculation zone-thus necessitating the deliberate design and artificial construction of a 2D flame structure. Moreover, inherent micro-scale challenges, such as the high surface-tovolume ratio, excessive wall heat loss, and radical quenching, are further amplified, rendering its combustion mechanism far more complex than that of traditional 3D counterparts. Experimental results show notable performance advantages: as the total flow rate increases from 0.20 L/min to 0.40 L/min, the stability limit (equivalence ratio range) expands from 0.404 to 2.60 to 0.095-3.73; peak temperatures reach 728 K (surface) and 1826 K (simulated core); relative heat loss drops from 95.4% to 49.6%. Numerical simulations reveal a fully developed swirl field at high flows, forming a unique "annular anchoring-radial diffusion" flame structure (anchored along the chamber's edge arc) that avoids central overheating and uneven temperature distribution plaguing cylindrical combustors. Bayesian network data mining identifies a "Velocity -> H2 -> Structure -> Homogeneous/Heterogenous" causal chain (test-set precision: 0.7187), underscoring the need for multi-physical field analysis. This disk-type design overcomes cylindrical limitations, providing a scalable, vane-free solution for high-efficiency micro-combustion.
Securing platinum-group metals (PGMs) for low-carbon technologies requires recycling routes that are both efficient and deployable near the source of waste. We report a fully electrified, modular flowsheet that couples microwave-assisted leaching (MWAL) with gas-diffusion electrocrystallization (GDEx) to recover and upcycle PGMs from spent automotive catalysts. Using 1.5 M HCl + 1.5 M NaCl at up to 200 degrees C and L/S <= 10, MWAL achieves >= 90% PGM leaching while suppressing matrix dissolution at lower L/S. The resulting multielement leachates are treated at room temperature by GDEx (10-200 mA cm(-2)), which selectively converts dissolved PGMs into similar to 5 nm alloy nanoparticles of similar to 83-91 wt% purity with >95% metal conversion. The as-recovered nanoparticles act directly as electrocatalysts for the methanol oxidation reaction, delivering MA = 241.7 mA mg(Pt)(-1), SA = 0.42 mA cm(-2), and similar to 85% activity retention after 1000 cycles. Closed-loop leachate reuse between GDEx and MWAL preserves PGM extraction while progressively reducing matrix co-leaching. Life-cycle assessment identifies MWAL electricity as the primary hotspot (similar to half of the average impacts), followed by GDEx effluent neutralisation. A 30% heat recovery in MWAL and low-carbon electricity reduces burdens (up to 60% in climate change). A techno-economic assessment at 50 L h(-1) treatment capacity indicates robust viability (operating margin 13.9%, NPV = 9.08 M & euro;, IRR = 45.1%, 4 year payback), with further gains from scale-up and valorising the nanoparticle product. The MWAL-GDEx route thus offers a scalable, decentralisation-ready, electrified pathway that recovers PGMs with high efficiency and directly upgrades them into functional catalysts.
