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Volcanic-Size-Dependent Activity Trends in Ru-Catalyzed Alkaline Hydrogen Evolution Reaction
No full textThe alkaline hydrogen evolution reaction (HER) plays a pivotal role in realizing a H-2-based circular economy. However, it is a topic of ongoing debate because of its complexity. Here, we unveil a unique volcano-type size-dependent activity trend of archetypical Ru-based alkaline HER catalysts and demonstrate that this trend is dictated by the interplay of geometric and electronic effects. Size-controlled Ru nanoparticles (NPs) from 0.78 to 3.31 nm were synthesized, and they exhibited volcanic size-dependence of specific activity and reaction kinetics, with 1.38 nm Ru NPs showing the highest activity and lowest Tafel slope. The large variation in the Tafel slope of Ru NP catalysts from 29 to 109 mV dec(-1) suggests that tuning of Ru NP size can vary the rate-determining step in the order of the Heyrovsky (0.79 nm), Tafel (1.38 nm), and Volmer (3.31 nm) steps. The specific activity of 1.38 nm Ru NPs is 5.0, 2.6, and 1.2 times higher than those of 3.31 nm Ru NPs and commercial Pt/C and Ru/C catalysts, respectively. Atomic-level geometric structure analysis and density functional theory calculations revealed that excellent activity of 1.38 nm Ru NPs correlates with their abundance in edge sites, which show optimum H* binding energy and elementary step energetics. A significant decline in the intrinsic activity of the alkaline HER was observed in the subnanometer Ru NPs, which could be associated with suppressed H* chemisorption due to enhanced surface oxidation and amorphous surface nature in this size regime. Overall, the intrinsic activity trend of Ru NPs is governed by both the geometric fraction of active edge sites and suppression of H* intermediate chemisorption in the subnanometer regime
Sustainable cementitious composites with 30% porosity and a compressive strength of 30 MPa
No full textMany researchers have tried to increase the porosity of cement-based materials for different applications, but a limitation of the existing technology is that it is difficult to achieve more than 30 MPa compressive strength for materials that have a porosity of more than 30%. To overcome the decrease in compressive strength, some studies have devel -oped fly ash-based foam geopolymers with silica fume as the foaming agent. However, this material requires heat curing and has a rapid setting problem. Therefore, the present study aimed to develop a material that can maintain compressive strength above 30 MPa while increasing the porosity to 30%, solving the curing problem, and extending the setting time. This study proposes a sustainable material design based on the concept of limestone calcined clay cement (LC3) and a fly ash-based foamed geopolymer. The results show that the proposed material can generate porosity of more than 30% and maintain a compressive strength above 30 MPa while the rapid setting and curing limitation problems are solved. Moreover, the developed cementitious composite was proven to reduce CO2 emissions by 31.91% compared to conventional construction materials, which highlights that the newly developed material can be classified as a low carbon construction material.(c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Direct visualization of replication and R-loop collision using single-molecule imaging
No full textR-loops are three-stranded nucleic acid structures that can cause replication stress by blocking replication fork progression. However, the detailed mechanism underlying the collision of DNA replication forks and R-loops remains elusive. To investigate how R-loops induce replication stress, we use single-molecule fluorescence imaging to directly visualize the collision of replicating Phi29 DNA polymerase (Phi29 DNAp), the simplest replication system, and R-loops. We demonstrate that a single R-loop can block replication, and the blockage is more pronounced when an RNA???DNA hybrid is on the non-template strand. We show that this asymmetry results from secondary structure formation on the non-template strand, which impedes the progression of Phi29 DNAp. We also show that G-quadruplex formation on the displaced single-stranded DNA in an R-loop enhances the replication stalling. Moreover, we observe the collision between Phi29 DNAp and RNA transcripts synthesized by T7 RNA polymerase (T7 RNAp). RNA transcripts cause more stalling because of the presence of T7 RNAp. Our work provides insights into how R-loops impede DNA replication at single-molecule resolution
Platelet Membrane???Enclosed Bioorthogonal Catalysis for Combating Dental Caries
No full textPlatelets have shown promise as a means to combat bacterial infections, fostering the development of innovative therapeutic approaches. However, several challenges persist, including cargo loading issues, limited efficacy against biofilms, and concerns regarding the impact of payloads on the platelet carriers. Here, human platelet membrane vesicles (h-PMVs) encapsulating supramolecular metal catalysts (SMCs) as ???nanofactories??? to convert prodrugs into antimicrobial compounds within close proximity to bacteria are introduced. Having established the feasibility and effectiveness of the SMCs within h-PMVs, referred to as the PLT-reactor, to activate pro-antibiotic drugs (pro-ciprofloxacin and pro-moxifloxacin) using model organisms (Escherichia coli ATCC 25922 and Staphylococcus aureus ATCC 25923), the investigation is subsequently extended to oral biofilms, with a particular emphasis on Streptococcus mutans 3065. This ???bind and kill??? strategy demonstrates the potent antimicrobial specificity of the PLT-reactor through localized antibiotic production. h-PMVs play a pivotal role by enabling precise targeting of pathogenic biofilms on natural teeth while minimizing potential hemolytic effects. The finding indicates that platelet membrane-cloaked surfaces exhibit robust, multifaceted, and pathogen-specific binding affinity with excellent biocompatibility, making them a promising alternative to antibody-based therapies for infectious diseases
Immobilizing Natural Photosystem II into TiO2 Photoanode with PEl hydrogel for Solar Water Oxidation
No full textBoosting Square Tensile Strain to Promote Functional 0-dimensional Defect Dipoles and Reversible Control of Ternary Polar States
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A multi-domain mixture density network for tool wear prediction under multiple machining conditions
No full textAccurate tool wear prediction is an essential task in machining processes because it helps to schedule efficient tool maintenance and maximise the tool's useful life, thus contributing to sustainable production via zero defect manufacturing (ZDM). However, there are limitations to existing methods; these cannot be used under multiple machining conditions, which is common practice. This problem not only hinders accurate tool wear monitoring but also necessitates the use of multiple models, which increases operation and modelling costs. Therefore, the multi-domain learning problem should be addressed to enable tool wear prediction under various machining conditions. To this end, this work presents a novel method, a multi-domain mixture density network (MD2
N). In particular, a Bayesian learning-based feature extractor is proposed to learn domain-invariant representations. Additionally, an adversarial learning approach is developed to lead the predictive model in learning domain-invariant features. Lastly, a mixture density network-based predictor is used to generate probabilistic tool wear outputs. Experiments that use datasets from real-world milling processes under multiple conditions prove the proposed method's promising efficacy, with the best mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) of 2.1748, 5.6422, and 0.0350, respectively, indicating the ability to learn multi-domain representations
Out-of-Autoclave Process for the Fabrication of an Aircraft Window Frame Using Recycled LM/PAEK Thermoplastic Composites
No full textCan We Use Diffusion Probabilistic Models for 3D Motion Prediction?
No full textAfter many researchers observed fruitfulness from the recent diffusion probabilistic model, its effectiveness in image generation is actively studied these days. In this paper, our objective is to evaluate the potential of diffusion probabilistic models for 3D human motion-related tasks. To this end, this pa-per presents a study of employing diffusion probabilistic models to predict future 3D human motion(s) from the previously observed motion. Based on the Human 3.6M and HumanEva-I datasets, our results show that diffusion probabilistic models are competitive for both single (deterministic) and multiple (stochastic) 3D motion prediction tasks, after finishing a single training process. In addition, we find out that diffusion probabilistic models can offer an attractive compromise, since they can strike the right balance between the likelihood and diversity of the predicted future motions. Our code is publicly available on the project website: https://sites.google.com/view/diffusion-motion-prediction