Hasil untuk "Chemical engineering"

Meaghan E. Germain, M. J. Knapp

Todd C. Holmes, S. Lacalle, Xing Su et al.

L. Hung, C.-H. Chen

D. Evans, H. Wennerström

E. Gazit

I. Levental, Penelope C. Georges, P. Janmey

R. Langer, N. Peppas

Shuai Yuan, Jun-sheng Qin, Christina T. Lollar et al.

Group 4 metal-based metal–organic frameworks (MIV-MOFs), including Ti-, Zr-, and Hf-based MOFs, are one of the most attractive classes of MOF materials owing to their superior chemical stability and structural tunability. Despite being a relatively new field, MIV-MOFs have attracted significant research attention in the past few years, leading to exciting advances in syntheses and applications. In this outlook, we start with a brief overview of the history and current status of MIV-MOFs, emphasizing the challenges encountered in their syntheses. The unique properties of MIV-MOFs are discussed, including their high chemical stability and strong tolerance toward defects. Particular emphasis is placed on defect engineering in Zr-MOFs which offers additional routes to tailor their functions. Photocatalysis of MIV-MOF is introduced as a representative example of their emerging applications. Finally, we conclude with the perspective of new opportunities in synthesis and defect engineering.

D. Huh, Y. Torisawa, G. Hamilton et al.

C. C. Ikeagwuani, D. C. Nwonu

Abstract Expansive soils are problematic due to the performances of their clay mineral constituent, which makes them exhibit the shrink-swell characteristics. The shrink-swell behaviours make expansive soils inappropriate for direct engineering application in their natural form. In an attempt to make them more feasible for construction purposes, numerous materials and techniques have been used to stabilise the soil. In this study, the additives and techniques applied for stabilising expansive soils will be focused on, with respect to their efficiency in improving the engineering properties of the soils. Then we discussed the microstructural interaction, chemical process, economic implication, nanotechnology application, as well as waste reuse and sustainability. Some issues regarding the effective application of the emerging trends in expansive soil stabilisation were presented with three categories, namely geoenvironmental, standardisation and optimisation issues. Techniques like predictive modelling and exploring methods such as reliability-based design optimisation, response surface methodology, dimensional analysis, and artificial intelligence technology were also proposed in order to ensure that expansive soil stabilisation is efficient.

C. Schmidt, Jennie M. Baier

C. Verma, E. Ebenso, M. Quraishi et al.

Recently, research studies in the fields of science and engineering are directed towards the synthesis, design, development, and consumption of environment-friendly chemical species to replace traditional toxic chemicals.

M. Rahmati, Eduardo A. Silva, J. Reseland et al.

Biomedical scientists use chemistry-driven processes found in nature as an inspiration to design biomaterials as promising diagnostic tools, therapeutic solutions, or tissue substitutes. While substantial consideration is devoted to the design and validation of biomaterials, the nature of their interactions with the surrounding biological microenvironment is commonly neglected. This gap of knowledge could be owing to our poor understanding of biochemical signaling pathways, lack of reliable techniques for designing biomaterials with optimal physicochemical properties, and/or poor stability of biomaterial properties after implantation. The success of host responses to biomaterials, known as biocompatibility, depends on chemical principles as the root of both cell signaling pathways in the body and how the biomaterial surface is designed. Most of the current review papers have discussed chemical engineering and biological principles of designing biomaterials as separate topics, which has resulted in neglecting the main role of chemistry in this field. In this review, we discuss biocompatibility in the context of chemistry, what it is and how to assess it, while describing contributions from both biochemical cues and biomaterials as well as the means of harmonizing them. We address both biochemical signal-transduction pathways and engineering principles of designing a biomaterial with an emphasis on its surface physicochemistry. As we aim to show the role of chemistry in the crosstalk between the surface physicochemical properties and body responses, we concisely highlight the main biochemical signal-transduction pathways involved in the biocompatibility complex. Finally, we discuss the progress and challenges associated with the current strategies used for improving the chemical and physical interactions between cells and biomaterial surface.

K. Reichenbächer, Heike I. Süss, J. Hulliger

Nasir Shezad, Avik De, Ajaikumar Samikannu et al.

The catalytic conversion of CO₂ into methane (CH₄) offers a sustainable solution to the worsening global warming scenario, especially for controlling CO₂ levels. This study reports silicalite-1 supported Ni catalysts with different loadings for CO₂ conversion to CH₄, prepared via wet impregnation. The X-ray diffraction pattern revealed an increase in crystallite size at higher Ni loadings, which was further supported by N₂ sorption, where the specific surface area and microporosity of the catalysts were decreased. There was a slight shift in the reducibility of the catalysts, potentially indicating the impact of loading on dispersion and spatial distribution. The catalyst performance was evaluated over a range of temperatures at 5 bar and a GHSV of 20,000 mL g_cat⁻¹ h⁻¹. Surprisingly, the Ni(5)@Silicalite-1 exhibited higher CO₂ conversion efficiency across the range of temperatures compared to Ni(10)@Silicalite-1. The NiO(5)@Silicalite-1 demonstrated a maximum CO₂ conversion of 88% at 450 °C, which was approximately 14% higher than that of the catalyst with a 10 wt.% loading. Notably, the CH₄ selectivity pattern was quite identical across the catalysts, underscoring that the reaction pathways were unaffected by the loadings. The higher performance of NiO(5)@Silicalite-1 could be ascribed to smaller NiO crystallites and improved textural properties.

Jiahong Xiang, Wenxiao He, Xihua Wang et al.

The Rust programming language presents a steep learning curve and significant coding challenges, making the automation of issue resolution essential for its broader adoption. Recently, LLM-powered code agents have shown remarkable success in resolving complex software engineering tasks, yet their application to Rust has been limited by the absence of a large-scale, repository-level benchmark. To bridge this gap, we introduce Rust-SWE-bench, a benchmark comprising 500 real-world, repository-level software engineering tasks from 34 diverse and popular Rust repositories. We then perform a comprehensive study on Rust-SWE-bench with four representative agents and four state-of-the-art LLMs to establish a foundational understanding of their capabilities and limitations in the Rust ecosystem. Our extensive study reveals that while ReAct-style agents are promising, i.e., resolving up to 21.2% of issues, they are limited by two primary challenges: comprehending repository-wide code structure and complying with Rust's strict type and trait semantics. We also find that issue reproduction is rather critical for task resolution. Inspired by these findings, we propose RUSTFORGER, a novel agentic approach that integrates an automated test environment setup with a Rust metaprogramming-driven dynamic tracing strategy to facilitate reliable issue reproduction and dynamic analysis. The evaluation shows that RUSTFORGER using Claude-Sonnet-3.7 significantly outperforms all baselines, resolving 28.6% of tasks on Rust-SWE-bench, i.e., a 34.9% improvement over the strongest baseline, and, in aggregate, uniquely solves 46 tasks that no other agent could solve across all adopted advanced LLMs.

Yuchen Zhu, Donghai Zhao, Yangyang Zhang et al.

Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We introduce SpaceGFN, a generative framework that elevates chemical space to a programmable computational object: a controllable degree of freedom enabling explicit construction and adaptive traversal of structured molecular universes. SpaceGFN decouples space definition from exploration. Users specify building blocks and reaction rules to construct chemically and synthetically coherent spaces, while a GFlowNet performs efficient, property-biased sampling within them. In Discovery mode, we demonstrate programmable space design through two strategies. A pseudo-natural product space assembles natural product-like architectures. An evolution-inspired (Evo) space recombines endogenous metabolite fragments via enzyme-consistent transformations, introducing an evolutionary prior into chemical generation. This bias yields favorable shifts in predicted metabolic and toxicological profiles while preserving pharmacological diversity, supported by broad docking enrichment across therapeutic targets. In Editing mode, SpaceGFN enables reaction-consistent lead optimization through a curated toolkit of executable synthetic transformations, allowing local, synthesis-aware modification of existing compounds instead of unrestricted graph mutation. Across 96 drug targets, SpaceGFN achieves strong optimization performance while maintaining structural diversity under synthetic constraints. By integrating programmable chemical universe construction with flow-based exploration and reaction-level editing, SpaceGFN establishes a general paradigm for deliberate navigation of therapeutic chemical space.

Jiashen Tang, Jiashen Tang, Connor A. Roncaioli et al.

Ensembles of nitrogen-vacancy (NV) centers in diamond are versatile quantum sensors with broad applications in the physical and life sciences. The concentration of neutral substitutional nitrogen ([Ns0]) strongly influences NV electronic spin coherence times, sensitivity, and optimal sensing strategies. Diamonds with [Ns0] ∼ 1–10 ppm are a focus of recent material engineering efforts, with higher concentrations being favorable for continuous-wave optically detected magnetic resonance (CW-ODMR) and lower concentrations expected to benefit pulsed magnetometry techniques through extended NV spin coherence times and improved sensing duty cycles. In this work, we synthesize and characterize low-[Ns0] (∼0.8 ppm), NV-enriched diamond material, engineered through low-strain chemical vapor deposition (CVD) growth on high-quality substrates, 12C isotopic purification, and controlled electron irradiation and annealing. Our results demonstrate good strain homogeneity in diamonds grown on CVD substrates and spin-bath-limited NV dephasing times. By measuring NV spin and charge properties across a wide range of optical NV excitation intensity, we provide direct comparisons of photon-shot-noise-limited magnetic field sensitivity between the current low-[Ns0] and previously studied higher-[Ns0] (∼14 ppm) NV-diamond sensors. We show that low-[Ns0] diamond can outperform higher-[Ns0] diamond at moderate and low optical NV excitation intensity. Our results provide practical benchmarks and guidance for selecting NV-diamond sensors tailored to specific experimental constraints and sensing requirements.

Krit Rattanawonsakul, George Seleiro, Victoria Workman et al.

Abstract Liquid formulations of platelet-rich fibrin (Liquid-PRF) have been shown to promote oral soft tissue healing for some clinical applications, however, the efficacy of liquid-PRF as a standalone treatment remains uncertain. The aim of the present study was to investigate the effects of liquid-PRF on oral cells in vitro using two-dimensional cell culture and three-dimensional tissue-engineered oral mucosa models. Media was conditioned with liquid-PRF prepared from blood samples and applied to oral fibroblasts, keratinocytes and tissue-engineered oral mucosa models. Metabolic activity, migration, proliferation and epithelial morphology were assessed. Liquid-PRF was shown to be biocompatible, with no cytotoxic effects observed on oral mucosa cells or 3D oral mucosa models. Cytokine analysis confirmed the presence of key growth factors, including PDGF-BB, TGF-β1, and EGF. Liquid-PRF increased oral fibroblast proliferation and promoted keratinocyte migration in 2D cultures. In tissue-engineered oral mucosa models, liquid-PRF showed no significant improvement in metabolic activity, epithelium thickness, morphology or proliferative capacity. The results suggest that growth factors in liquid-PRF were able to stimulate the proliferation and migration of oral mucosa cells in 2D culture, however these effects could not be demonstrated in 3D oral mucosa models. Factors secreted from liquid PRF were able to support the growth of cells and the development and maintenance of a healthy epithelium. Despite improvements in keratinocyte migration and fibroblast proliferation the results from 3D models indicate that factors secreted from liquid-PRF alone may not be sufficient to stimulate oral soft tissue repair.

Marco Eckhoff, Markus Reiher

Recent developments in computational chemistry facilitate the automated quantum chemical exploration of chemical reaction networks for the in-silico prediction of synthesis pathways, yield, and selectivity. However, the underlying quantum chemical energy calculations require vast computational resources, limiting these explorations severely in practice. Machine learning potentials (MLPs) offer a solution to increase computational efficiency, while retaining the accuracy of reliable first-principles data used for their training. Unfortunately, MLPs will be limited in their generalization ability within chemical (reaction) space, if the underlying training data are not representative for a given application. Within the framework of automated reaction network exploration, where new reactants or reagents composed of any elements from the periodic table can be introduced, this lack of generalizability will be the rule rather than the exception. Here, we therefore evaluate the benefits of the lifelong MLP concept in this context. Lifelong MLPs push their adaptability by efficient continual learning of additional data. We propose an improved learning algorithm for lifelong adaptive data selection yielding efficient integration of new data while previous expertise is preserved. In this way, we can reach chemical accuracy in reaction search trials.