337 research outputs found
Emilia. A tragedy. By Mark Anthony Meilan [electronic resource].
No full textThe imprint is not separated by double rules from the title.Variant: the imprint is thus separated.Also issued as part of: 'The dramatic works of Mark Anthony Meilan', London, [1780?].Electronic reproduction.English Short Title Catalog,Reproduction of original from British Library
Cover picture: NH4Br-assisted two-step-processing of guanidinium-rich perovskite films for extremely stable carbon-based perovskite solar cells in ambient air
No full textPerovskite Solar CellsIn article number 2101103, Yan-Zhen Zheng, Meilan Huang, Xia Tao, and co-workers fabricated NH4Br-assisted rich guanidinium perovskite films that allow inducing the formation of the intermediate phase NH4PbI3 and alleviating the disorder of the octahedron caused by an alien cation. The resultant films behave extremely well in photovoltaic performance upon assembly as carbon-based perovskite solar cells
CCDC 2061103: Experimental Crystal Structure Determination
No full textRelated Article: Chaoyi Yao, Hongyu Lin, Brian Daly, Yikai Xu, Warispreet Singh, H. Q. Nimal Gunaratne, Wesley R. Browne, Steven E. J. Bell, Peter Nockemann, Meilan Huang, Paul Kavanagh, A. Prasanna de Silva|2022|J.Am.Chem.Soc.|144|4977|doi:10.1021/jacs.1c1302
Effect of Adding Monohydrocalcite on the Microstructural Change in Cement Hydration
Get PDFCorrection: Correction to “Effect of Adding Monohydrocalcite on the Microstructural Change in Cement Hydration” at https://doi.org/10.1021/acsomega.2c08229 The authorship has changed: Wanawan Pragot regrettably published the work that she carried out during her Ph.D. at the University of Aberdeen under the supervision of Waheed Afzal where this work had been assisted by (then) post-doc Ara Carballo-Meilan and (then Ph.D. student) Lewis McDonald. Wanawan Pragot regrets her action and wants to correct it. She notes that Chaiwat Photong helped her in proofreading the manuscript before she submitted. We believe that the contribution of Chaiwat Photong does not merit being the first author. The revised order and new additions reflect the contributions. The Acknowledgment and Author contributions have also been revised as given here. Author Contributions The manuscript was written through contributions of all authors. W.P. carried out work and wrote the paper. C.P. reviewed and edited the writing. All authors have given approval to the final version of the manuscript. Notes The authors declare no competing financial interest. ACKNOWLEDGMENTS The authors wish to acknowledge Dr. Lewis J. McDonald and Dr. M. Ara Carballo-Meilan to share their experience and give the good advice Funding Information: The authors wish to thank the ACEMAC Facility at the University of Aberdeen for Electron Microscopy, the late Dr. Mohammed Imbabi, and Prof. Fred Glasser for fruitful discussions related to carbon capture, mineralogy, and cement chemistry. Wanawan Pragot acknowledges the Ministry of Science, Technology and Environment, Government of Thailand, for providing her a Ph.D. scholarship to study at the University of Aberdeen where the work was carried out. Publisher Copyright: © 2023 The Authors. Published by American Chemical Society.=Peer reviewe
Dynamic model and control of vehicles
Get PDFIn this thesis the author develops a 14 degrees-of-freedom (DOF) full-car model. The
model draws from and improves upon features and setups of certain existing vehicle
dynamics models. The proposed model provides a means to simulate vehicle ride and
handling behaviors. An accurate prediction of such behaviors will lead to the proper
control and design of vehicles.
The vehicle’s kinematics and dynamics are developed to reflect the interactions
between the rigid mass elements of the model such as the vehicle body and the wheels.
The mathematical model includes the nonlinear characteristics of the tires, the three
dimensional motions of the sprung and unsprung masses, the inertial coupling between
the sprung and unsprung masses, and the restraints and forces imposed by the suspension
components. The frictional forces developed at the road-tire contacts are modeled by the
single point contact version of the Lund-Grenoble (LuGre) dynamic friction model. An
extension of the LuGre friction model is presented to take into account the coupling
between the rotational and translational motions of the wheels.
Three different numerical study cases are selected to verify the model’s capability in
representing various vehicle dynamic situations with respect to the model’s accuracy and
to the model’s range of applicability.
The issue of active suspension is subsequently discussed. A non-switching sliding
mode controller is incorporated into the proposed vehicle model and a substantial
reduction in the spectral intensity of a vibration mode of the vehicle body is achieved.
Simulation results suggest that the rigorous modeling and mathematical
development yields a model that captures satisfactory ride comfort and vehicle
performance
Sustainable fashion : capsule collection to reinforce awareness in a new market (China)
No full textLAUREA MAGISTRALERecently, on one hand, people are living in "dolcevita" with ever-increasing material wealth, on the other hand, animas are living in a split personality that full of Tragedy, fear and conflict fight by own greed and decline environment. This research focus on pollution of fashion industry, from the perspective of a fashion designer, determine a suitable and feasible way to stay with pollution for myself, it not a common mode for everyone, but i would like to share it with everyone i meet, because, everyone's path to find
their own inner "God" is different but the common right way is living as a part of the whole world holistically.what i do in this paper is just a inspiratio for others
BioStructNet: Structure-Based Network with Transfer Learning for Predicting Biocatalyst Functions
No full textBioStructNet is a structure-based deep learning model designed to enhance the prediction of enzyme-substrate interactions, particularly focusing on biocatalysis. It includes transfer learning approaches for small, function-based datasets. The parameters are validated through molecular docking and MD simulations. Overview: BioStructNet is a deep learning framework developed to predict enzyme-substrate interactions by leveraging protein and ligand structural data. It employs Graph Neural Networks (GNNs) and Transformer-based modules to process protein structures and ligand molecules. This repository contains code for training and transfer learning. BioStructNet is especially useful for predicting biocatalytic activities (e.g., Kcat values) and enhancing function-based prediction with small dataset, such as in plastic degradation by enzymes like Candida antarctica lipase B (CalB). Features: GNN-based Protein and Ligand Encoders: Encodes protein structures using contact maps and ligand molecules in SMILES format. BCN Interaction Module: Captures local dependencies between protein-ligand pairs using Bilinear Co-Attention Networks (BCN). Transformer-based Interaction Module: Handles long-range dependencies for protein-ligand interactions using multi-head attention. Transfer Learning: Fine-tune pre-trained models on small function-specific datasets to enhance prediction accuracy with three fine-tuning methods, block, free, and LoRa. Molecular Docking Validation: Integrates docking simulations to compare learned interaction maps with physical conformations of protein-ligand complexes. If you use BioStructNet in your research, please cite the following: Wang, X., Zhou, J., Quinn, D., Moody, T., Huang, M. "Enhancing Function-Based Biocatalysis Prediction through a Structure-Based Deep Learning Approach.
Engineering of industrial biocatalysts
Get PDFIn the last 40 years advances in the protein engineering have prompted the application of biocatalysts in the synthesis of building blocks, fine and bulk active pharmaceutical chemicals for the agrochemical, food, biofuel and pharmaceutical industries. Computational chemistry methodologies are fueling the development of a new generation of rationally designed biocatalysts with enhanced selectivity and specificity at a fraction of the time and cost compared to traditional protocols such as directed evolution.
We present two examples of rational enzyme design. Our first example is the study of the phenylacetone monooxygenase (PAMO), the most stable and thermo-tolerant member of the Baeyer–Villiger monooxygenases family. We solved the catalytic mechanism of this enzyme for the native substrate phenylacetone as well as for a linear non-native substrate 2-octanone, using molecular dynamics simulations, quantum mechanics and quantum mechanics/molecular mechanics calculations.1 By studying relevant PAMO variants we provide a theoretical basis for the preference of the enzyme for the native aromatic substrate over non-native linear substrates.2
The second example regards an (S)-selective-transaminase from Vibrio fluvialis (S-TAm), which offers an environmentally sustainable synthesis route for the production of pure chiral amines.3 By applying a rational enzyme engineering protocol we altered this enzyme towards better acceptance of bulky ketones, starting with no detectable activity of the WT. Our best S-TAm variant improved the reaction rate by \u3e 1716-fold and retained activity even at 50 °C. To obtain such an outstanding result we only screened 113 variants, a substantially lower number than those typically associated with directed evolution (104 to 107 clones).
Both studies provide fundamental insights into the rational engineering of enzymes for industrial applications.
(1) Carvalho, A. T. P.; Dourado, D. F. A. R.; Skvortsov, T.; Abreu, M. de; Ferguson, L. J.; Quinn, D. J.; Moody, T. S.; Huang, M. Catalytic Mechanism of Phenylacetone Monooxygenases for Non-Native Linear Substrates. Phys. Chem. Chem. Phys. 2017, 19 (39), 26851–26861. https://doi.org/10.1039/C7CP03640J.
(2) Carvalho, A. T. P.; Dourado, D. F. A. R.; Skvortsov, T.; Abreu, M. de; Ferguson, L. J.; Quinn, D. J.; Moody, T. S.; Huang, M. Spatial Requirement for PAMO for Transformation of Non-Native Linear Substrates. Phys. Chem. Chem. Phys. 2018, 20 (4), 2558–2570. https://doi.org/10.1039/C7CP07172H.
(3) Dourado, D. F. A. R.; Pohle, S.; Carvalho, A. T. P.; Dheeman, D. S.; Caswell, J. M.; Skvortsov, T.; Miskelly, I.; Brown, R. T.; Quinn, D. J.; Allen, C. C. R.; Huang, M; Moody, T. Rational Design of a (S)-Selective-Transaminase for Asymmetric Synthesis of (1S)-1-(1,1′-Biphenyl-2-Yl)Ethanamine. ACS Catal. 2016, 6 (11), 7749–7759. https://doi.org/10.1021/acscatal.6b0238
Predicting the biocatalytic activities of enzymes by binding mode analysis and machine learning
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