bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surface Engineering of 2D Metal‐Porphyrin Metal‐Organic Frameworks Z‐Scheme Heterostructure for Boosting and Stable Photocatalytic Hydrogen Evolution
Authors: Zhu Wenjun , Zhu Hu , Zhang Taiyang , Qin Lixia , Kang Shi‐Zhao , Li Xiangqing 
Published: January 2025

AbstractHow to improve the stability and activity of metal‐organic frameworks is an attractive but challenging task in energy conversion and pollutant degradation of metal‐organic framework materials. In this paper, a facile method is developed by fabricating titanium dioxide nanoparticles (TiO2 NPs) layer on 2D copper tetracarboxylphenyl‐metalloporphyrin metal‐organic frameworks with zinc ions as the linkers (ZnTCuMT‐X, “Zn” represented zinc ions as the linkers, the first “T” represented tetracarboxylphenyl‐metalloporphyrin (TCPP), “Cu” represented the Cu2+ coordinated into the porphyrin macrocycle, “M” represented metal‐organic frameworks, the second “T” represented TiO2 NPs layer, and “X” represented the added volume of n‐tetrabutyl titanate (X = 100, 200, 300 or 400)). It is found that the optimized ZnTCuMT‐200 showed greatly and stably enhanced H2 generation, which is ≈28.2 times and 47.0 times as high as those of the original ZnTCuM and TiO2, respectively. Combined with the results of free radical capture, X‐ray photoelectron spectra (XPS), electron spin resonance (ESR), and theoretical calculation, a direct Z‐scheme electron transfer mechanism is achieved to fully explain the enhanced photocatalytic performance. It demonstrates that facilely designing Z‐scheme heterostructures based on porphyrin MOFs modified with an inorganic semiconductor layer can be an advantageous strategy for enhancing the stability and activity of photocatalytic hydrogen evolution.
CO2 and Temperature Induced Switching of a Flexible Metal–Organic Framework with Surface‐Mounted Nanoparticles
Authors: Berger Jan , Terruzzi Stephanie , Bunzen Hana , Ballerini Filippo , Vandone Marco , Marelli Marcello , Braglia Luca , Fischer Roland A. , Colombo Valentina , Kieslich Gregor 
Published: January 2025

AbstractWithin the material family of metal–organic frameworks (MOFs) the subclass of flexible MOFs (flexMOFs) has attracted great attention, showing structural flexibility as a response to external stimuli such as guest adsorption, temperature, and pressure. Hybrid composites like nanoparticle (NP) loaded flexible MOFs, which stand to potentially combine advantageous properties of both are yet largely unexplored. Here the synthesis of flexMOFs with surface mounted nanoparticles, e. g. NP@Zn2(BME‐bdc)2dabco composites (NP = Pt and SiO2 nanoparticles, BME‐bdc2− = 2,5‐bismethoxyethoxy‐1,4‐benzenedicarboxylate, dabco = 1,4‐diazabicyclo[2.2.2]octane) is reported, studying the impact of nanoparticles on the stimulus‐responsiveness of a flexMOF. It is shown that CO2 physisorption triggered flexibility of the MOF is retained and reversible for all NP@flexMOF composites. Additionally, it is observed that NPs stabilize the large pore state of the MOF, slightly increasing and shifting the switching pressure window. This effect is also observed during temperature‐induced switching but Pt@flexMOF composites partially lose long‐range order during the reversion to their narrow pore state, while attached SiO2 NPs allow for a fully reversible transition. These findings suggest that the total exerted material strain triggering the switching is heavily dependent on NP size and the applied stimulus and that guest‐induced switchability can be fully realized in NP@flexMOF hybrid materials.
Facile Fabrication of Monodisperse Vinyl Hybrid Core–Shell Silica Microsphere with Short Range Radial Channel in bi‐phase System
Authors: Wang Chenyang , Guo Tiantian , Tang Ruizhi , Ma Shujuan , Wei Yinmao , Bian Yangyang , Ou Junjie 
Published: January 2025

AbstractThe development of monodisperse hybrid silica microspheres with highly regular pore structure and uniform distribution of functional groups have significant value in the biomolecular separation field. In this work, the short range ordered pore channels are precisely constructed onto the non‐porous silica microsphere surface by a bi‐phase assembly method, and the cylindrical silica channel introduced a plethora of vinyl groups by “one‐pot” co‐condensation to form vinyl hybrid silica shell. As hydrophilic interaction chromatography (HILIC) stationary phase, the vinyl hybrid core–shell silica microsphere is simply modified with zwitterion glutathione (SiO2@SiO2‐GSH), in which the HILIC enrichment process is significantly shortened due to its specific porous characteristics. Most importantly, SiO2@SiO2‐GSH microsphere can enrich 2186 N‐glycopeptides from the rat liver protein digest within 2 min, which mapped to 806 glycoproteins. Compared with HILIC enrichment result within 1 h, the glycoproteins and glycopeptides overlap are 88.3% and 79.1%, performing excellent reproducibility. The short range ordered channels onto the silica microsphere surface exhibit excellent mass transfer efficiency, so the developed bi‐phase assembly method is expected to design more advanced hybrid silica materials for other urgently fields.
Hot Nanogap Networks‐In‐Triangular Nanoframes: A Strategy for Positioning Adsorbates Near Hot Spots
Authors: Lee Sungwoo , Zhao Qiang , Oh Seonmin , Lee Yujin , Choi Inyoung , Park Sungho 
Published: January 2025

AbstractThis study reports the synthesis of plasmonic hot nanogap networks‐in‐triangular nanoframes (NITNFs), featuring narrow intraparticle nanogap networks embedded within triangular nanoframes. Starting from Au nanotriangles, Pt NITNFs are synthesized through a cascade reaction involving simultaneous Pt deposition and Au etching in a one‐pot process. The Pt NITNFs are then transformed into plasmonically active Au NITNFs via Au coating. The near‐field focusing capabilities of the Au NITNFs are tailored by fine‐tuning the void area fraction down to 3.9%, resulting in the formation of narrow nanogaps of ≈1 nm. This optimization enables the successful implementation of single‐particle surface‐enhanced Raman scattering (SERS) measurements. Then, monolayer Au NITNFs films on Al substrates are prepared, which enabled weakly adsorbing species to be positioned close to the hot spots of the NITNFs by anchoring them to the underlying Al substrates. As a representative sensing application, the SERS‐based detection of gas‐phase dimethyl methylphosphonate (DMMP) using a film of plasmonic NITNFs on an Al substrate exhibits outstanding performances, achieving a limit of detection of 5 ppm and a detection time of 120 s.
Impact of Magneto‐Mechanical Actuation on Cell Differentiation: A Study Using Wireless, 3D‐Printed Device and a Porous Ferrogel
Authors: Basak Soumyadeep , Packirisamy Gopinath 
Published: January 2025

AbstractCells perceive external and internally generated forces of different kinds, significantly impacting their cellular biology. In the relatively nascent field of mechanobiology, the impact of such forces is studied and further utilized to broaden the knowledge of cellular developmental pathways, disease progression, tissue engineering, and developing novel regenerative strategies. However, extensive considerations of mechanotransduction pathways for biomedical applications are still broadly limited due to a lack of affordable technologies in terms of devices and simple magnetic actuatable materials. Herein, synthesizing a monophasic, macroporous, in situ‐fabricated gelatin‐based ferrogel is reported using polyethylene glycol (PEG) coated‐iron oxide (magnetite) particles with high magnetization. Developing a 3D printed, compact, and wireless device capable of providing a wide range of magneto‐mechanical actuation using magnetic field susceptible materials in a noncontact manner is reported. Using the device and ferrogel, C2C12 myoblast differentiation is studied under magnetic field actuation, and significant differences in the myogenin, a differentiation marker, expression behavior are observed. Due to careful design considerations, robust component selection, and easy availability of low‐cost precursor for magnetic responsive material fabrication, the device‐ferrogel combination can be easily adapted to routine biological studies, thereby helping mechanobiology to be utilized for developing exciting new biomedical strategies.
Engineered 3D DNA Crystals: A Molecular Design Perspective
Authors: Cai Baoshuo , Rong Xiao , Sun Yifan , Liu Longfei , Li Zhe 
Published: January 2025

AbstractRecent advances in biomolecular self‐assembly have transformed material science, enabling the creation of novel materials with unparalleled precision and functionality. Among these innovations, 3D DNA crystals have emerged as a distinctive class of macroscopic materials, engineered through the bottom‐up approach by DNA self‐assembly. These structures uniquely combine precise molecular ordering with high programmability, establishing their importance in advanced material design. This review delves into the molecular design of engineered 3D DNA crystals, classifying current crystal structures based on “crystal bond orientations” and examining key aspects of in‐silico molecular design, self‐assembly, and crystal modifications. The functionalization of 3D DNA crystals for applications in crystallization scaffolding, biocatalysis, biosensing, electrical and optical devices, as well as in the emerging fields of DNA computing and data storage are explored. Finally, the ongoing challenges are addressed and future directions to advance the field of engineered 3D DNA crystals are proposed.
Study on the distribution characteristics of damage and multidimensional parameter influence of abrasive water jet impact on reinforced concrete
Authors: Liu Renjie , Long Wanpeng , Qin Qin , Hou Xiaowei , Li Jing , Luo Zixian , Zhu Bin 
Published: January 2025

AbstractAbrasive water jet (AWJ) crushing technology holds significant promise for concrete maintenance and demolition. However, challenges such as high energy consumption and mismatched parameters require urgent optimization to enhance crushing efficiency. To address this, this paper proposes a novel method combining smoothed particle hydrodynamics (SPH) and the finite element method (FEM) to develop a numerical model for reinforced concrete (RC) subjected to AWJ impact. The study explores the damage distribution characteristics and the impact of various parameters on RC under AWJ conditions. The results reveal that the axial damage reduction in RC progresses through three stages: rapid reduction, slow reduction, and the slowest reduction. In contrast, the longitudinal damage reduction process consists of rapid and slow reduction stages. Furthermore, within the studied parameter range, the comprehensive damage factor of RC increases rapidly and then stabilizes as the jet velocity rises. As the nozzle diameter increases, the comprehensive damage factor initially decreases sharply and then increases rapidly. With increasing abrasive concentration and stand‐off distance, the comprehensive damage factor rises quickly before declining.
Utilizing Machine Learning for Early Intervention and Risk Management in the Opioid Overdose Crisis
Authors: Tai Andy Man Yeung , Kazemi Alireza , Kim Jane J. , Schmeckenbecher Jim , Kitchin Vanessa , Suen Janet , Moro Ryan , Krausz R. Michael 
Published: January 2025

ABSTRACTThis systematic review and meta‐analysis seek to identify prevalent machine learning (ML) models applied to outcomes related to illicit opioid use. Following PRISMA guidelines, we reviewed databases including MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science, yielding 10,666 records. Of these, 6029 were unique, leading to 155 full‐text publications, with 69 studies meeting inclusion criteria. The inclusion criteria focused on two primary themes: the application of artificial intelligence and machine learning techniques, and opioid related substance use outcomes. The meta‐analysis focused on Area Under the Receiver Operating Characteristic curve (AUC/AUROC). Most of the studies used classification models and evaluated them using the AUC metric. Cohen's d effect sizes were 1.22 for logistic regression (AUC = 0.806), 1.26 for decision trees/random forests (AUC = 0.814), 1.54 for deep learning (AUC = 0.862), and 1.27 for boosting algorithms (AUC = 0.815). Regarding outcomes, effect sizes were 1.42 for opioid use disorder (OUD) (AUC = 0.842), 1.37 for opioid overdoses (AUC = 0.842), and 1.25 for risk of drug use (AUC = 0.812). The study reveals the efficacy of ML in illicit opioid use, with a notable predominance of supervised ML models, particularly Logistic Regression. The underutilization of regression models, despite their potential in outcome quantification, was surprising. Deep learning emerged as the most effective model, demonstrating the complexity of data in addiction psychiatry. ML algorithms provide a powerful framework for informed decision‐making in addiction care, leading toward personalized medicine and reducing unregulated drug use and related harms.
Generalized kernel regularized least squares estimator with parametric error covariance
Authors: Dang Justin , Ullah Aman 
Published: January 2025

AbstractA two-step estimator of a nonparametric regression function via Kernel regularized least squares (KRLS) with parametric error covariance is proposed. The KRLS, not considering any information in the error covariance, is improved by incorporating a parametric error covariance, allowing for both heteroskedasticity and autocorrelation, in estimating the regression function. A two step procedure is used, where in the first step, a parametric error covariance is estimated by using KRLS residuals and in the second step, a transformed model using the error covariance is estimated by KRLS. Theoretical results including bias, variance, and asymptotics are derived. Simulation results show that the proposed estimator outperforms the KRLS in both heteroskedastic errors and autocorrelated errors cases. An empirical example is illustrated with estimating an airline cost function under a random effects model with heteroskedastic and correlated errors. The derivatives are evaluated, and the average partial effects of the inputs are determined in the application.
Optimizing Corbel Reinforcement Through Nonlinear Analysis: Determining Superiority of Steel Bars or CFRP
Authors: Kheyroddin Ali , Raygan Shakiba , Kioumarsi Mahdi 
Published: January 2025

AbstractThis study aims to optimize the carbon fiber-reinforced polymer’s (CFRP) arrangements rather than the amount of secondary steel reinforcements for strengthening reinforced concrete (RC) corbels, utilizing finite element (FE) analysis. While previous studies have only examined different types of FRP sheet arrangement in the absence or presence of equal amount of secondary steel reinforcements, this research fills a gap by comparing the effect of different configurations with the Externally Bonded Reinforcement (EBR) installation method and increasing the amount of the secondary steel reinforcements. Verification was conducted using a fully horizontally wrapped strengthened RC corbel with the secondary steel reinforcement. Results indicate that by just differing the arrangements of CFRP sheets, an increase can cause up to 33% in bearing capacity with the same volume of CFRP. However, by increasing the geometrical secondary steel reinforcement ratio (ρsh) from 0.18% of the verified model to 0.33% and 0.52%; the bearing capacity increases by 5.52% and 11.55%, respectively.
Intensity Measures for Flood Hazards in Fragility Assessments of Bridges
Authors: Torabi Tahereh , Shafei Behrouz , Alipour Alice 
Published: January 2025

AbstractThis chapter explores the significance of intensity measures for flood hazards in assessing the fragility of bridges. Flood hazards pose considerable challenges to bridge infrastructure due to factors such as hydrodynamic forces, scouring, and debris accumulation. The study reviews various methodologies and models used to quantify flood intensity, including hydraulic simulations, empirical equations, and computational fluid dynamics. Despite advancements in modeling techniques, challenges persist, including uncertainties in hazard prediction and gaps between research and practical applications. Importantly, the chapter highlights the need to incorporate climate change considerations into hazard assessments and infrastructure design, given the projected increase in the intensity and frequency of extreme weather events. The absence of climate change in existing studies underscores a critical area for future research and intervention. By integrating advancements in modeling techniques and proactive adaptation strategies, this study’s outcome promotes sustainable infrastructure development and ensures the longevity of bridge structures in the face of evolving environmental conditions.
Emergy Framework, from Building Stock Model to Retrofit Model: A Study in Mexico’s City Public Office Buildings
Authors: Flores Ivett 
Published: January 2025

AbstractThis chapter addresses a gap in the current literature by focusing on retrofit strategies for existing buildings, particularly on an urban scale, located in a temperate weather context. The proposed methodology consists of three steps (Synthetic Stock Generation, Building Stock Dynamics, and Building Stock Assessment) to create an “Archetype Building Information” framework grounded in an Emergy approach. Preliminary findings reveal consistent energy demand behavior among buildings of similar typologies, forming the basis for defining shared patterns. The difference ratio between baseline and recommended energy demands by ASHRAE, while variable, follows a distinct trend across building types. The research aims to develop a comprehensive Building Retrofit Model tailored to the unique challenges and opportunities in Mexico City’s urban context.
Citizen Engagement and Co-creation in a Net-Zero Built Environment Transitions: Challenges and Best Practices
Authors: Kunze Christian Wolfgang , Belay Alemu Moges , Hedar Ahmed Samir , Dandwate Aaditya 
Published: January 2025

AbstractTo address energy transition through a net-zero built environment, significant citizen activation and participation is crucial. The chapter aims to identify the challenges, outline best practices in citizen co-creation based on the results of selected EU H2020 projects, and provide future research directions. Best practices are analysed in three phases. (1) Recruitment Phase: The first phase of engagement encompasses all the moments in which potential users are exposed to information that is relevant to participating in a specific project or programme until the moment in which they sign up. Core success factors for the set-up of a promising recruitment phase will be outlined. (2) Consumer Response Phase: In the second consumer response phase, strategies are directed at initiating and consolidating responses to innovative net-zero projects. This phase starts with the user’s own acts of planning and reflecting on how best to integrate existing into changing and rearranging urban and domestic living perceptions. (3) Persistence Phase: In the third phase (i.e., persistence), engagement strategies seek to maintain interest and continuous involvement in programmes. By prolonging interest and interaction over time, the goal of this phase is to ensure that participation remains sufficiently interesting and comfortable for users, transferring them to ambassadors and multipliers for innovative solutions. The chapter provides a co-creation framework with project insight-based best practice recommendations and proposes a clustering of stakeholders according to a quintuple helix approach and a stepwise phase approach towards the set-up of net-zero built environment projects.
Evaluation of Train Travel Inside Submerged Floating Tunnel Under Wave Loads
Authors: Dai Jian , Acharya Paban 
Published: January 2025

AbstractRailway trains are an efficient mode of transportation for passengers and goods in large quantities. The planning of railway lines, however, needs to take the complex terrain into account. For areas with land parcels separated by wide and deep water bodies, the railway connection is even more challenging. Submerged floating tunnels (SFTs), on the other hand, become an appealing option to connect the separated land parcels. They utilize the natural water buoyancy to support the weight and payloads of the structure and traffic. They have a much lower dependency on the seabed conditions as opposed to their bottom-founded counterparts. They are also submerged at a certain depth below the water surface, which reduces the wave loads acting on the structure and provides a navigation clearance for passing vessels. Owing to the numerous advantages of such structural systems, they have attracted interest among both researchers and engineers. However, the research on the use of such transport infrastructure for safe and comfortable train travel is limited. In this paper, the dynamic responses of a train traveling across an SFT subjected to wave loads are examined. The SFT is modeled as a slender beam according to the Euler–Bernoulli beam theory. The hydrodynamic responses of the SFT subjected to various wave conditions are analyzed using SIMA/RIFLEX. Then, the responses are extracted as input to a train model idealized as a multi-body system for the calculation of the vehicle dynamics. This study aims to propose a computationally efficient method for the evaluation of the safety and comfort of train travel inside an SFT in an environmentally exposed location. Results show that travel rides inside SFTs are generally good under the normal design operational conditions. When the sea conditions are rough, limiting the operational train speeds may be necessary as the vertical acceleration is approaching 0.6 m/s2. Under extreme sea conditions, the SFTs should be closed to traffic.
The role of generative AI in academic and scientific authorship: an autopoietic perspective
Authors: Watson Steven , Brezovec Erik , Romic Jonathan 
Published: January 2025

Abstract
The integration of generative artificial intelligence (AI), particularly large language models like ChatGPT, presents new challenges as well as possibilities for scientific authorship. This paper draws on social systems theory to offer a nuanced understanding of the interplay between technology, individuals, society and scholarly authorial practices. This contrasts with orthodoxy, where individuals and technology are treated as essentialized entities. This approach offers a critique of the binary positions of sociotechnological determinism and accelerationist instrumentality while still acknowledging that generative AI presents profound challenges to existing practices and meaning making in scientific scholarship. This holistic treatment of authorship, integrity, and technology involves comprehending the historical and evolutionary entanglement of scientific individuality, scientific practices, and meaning making with technological innovation. This addresses current needs for more robust theoretical approaches to address the challenges confronted by academicians, institutions, peer review, and publication processes. Our analysis aims to contribute to a more sophisticated discourse on the ethical and practical implications of AI in scientific research.