Article
Biochemical Research Methods
Mehavesh K. Hameed, Javad B. M. Parambath, Sofian M. Kanan, Ahmed A. Mohamed
Summary: Biocompatible and luminescent nanostructures synthesized by capping gold-carbon nanoparticles with amino acids were successfully utilized for the quantitative estimation of the drug ranitidine. The method showed a detection limit of 0.174, 0.56, and 0.332 mu M for different amino acid bioconjugates, and was also successful in quantifying ranitidine in spiked serum samples. The study demonstrates the potential of this approach for sensitive and accurate drug quantification.
ANALYTICAL AND BIOANALYTICAL CHEMISTRY
(2021)
Article
Chemistry, Multidisciplinary
Shuting Xiao, Yi Yao, Shuilin Liao, Bin Xu, Xue Li, Yuxiao Zhang, Lei Zhang, Qiang Chen, Haoneng Tang, Qibin Song, Ming Dong
Summary: Researchers have developed an EV-MPDS system based on FRET signals, which allows convenient diagnosis of lung cancer without the need for EV extraction and purification. In clinical samples, this system demonstrated improved accuracy and sensitivity compared to the ELISA detection method, with early screening accuracy further enhanced through machine learning analysis of five biomarkers.
Article
Chemistry, Applied
Ashton N. Bartley, Sadie F. Depeter, Ronald K. Castellano
Summary: This article reports the preparation and photophysical investigation of the first molecular multi-FRET system from the benzotrifuranone (BTF) scaffold. The target molecule was synthesized using a step-conservative approach, incorporating three optically complementary chromophores. The study demonstrates high energy transfer efficiency and significant pseudo-Stokes shift in the fluorescence emission of the target. The design has potential applications in material science and life sciences.
Article
Chemistry, Analytical
Simiao Zhang, Ning Zhang, Shishuang Wang, Zhenshun Li, Weiqing Sun, Mei Zhou, Yurong Zhang, Long Wu, Jing Ma
Summary: A highly sensitive fluorescence probe based on rhodamine 6G-loaded and MnO2 nanosheets-coated mesoporous silica nanospheres was developed for the detection of biogenic amines (BAs) in fish or fish products. The probe utilized R6G as an energy donor and MnO2 nanosheets as an energy acceptor. The method showed a wide linear detection range and high sensitivity, and was successfully validated using spiked fish samples, demonstrating its potential application in food safety.
MICROCHEMICAL JOURNAL
(2023)
Article
Chemistry, Analytical
Muhammad Azhar Hayat Nawaz, Muhammad Waseem Fazal, Naeem Akhtar, Mian Hasnain Nawaz, Akhtar Hayat, Cong Yu
Summary: In this study, highly negatively charged zinc selenide (ZnSe) nanostructures were synthesized using a rational design approach. The ZnSe nanostructures were characterized and categorized into three types (A, B, and C) based on their synthesis method and solvent. The ZnSe-B6 type exhibited the highest fluorescence quenching effect and was used to develop an aptasensor for the detection of ochratoxin-A (OTA) in food samples.
Article
Chemistry, Analytical
Xianzhi Xu, Rong Xu, Shuang Hou, Zhaoqi Kang, Chuanjuan Lue, Qian Wang, Wen Zhang, Xia Wang, Ping Xu, Chao Gao, Cuiqing Ma
Summary: In this study, a fluorescent biosensor FILLac(10N0C) was developed for the selective detection of L-lactate levels. It showed high sensitivity and a low limit of detection, making it suitable for high-throughput detection in various biological samples.
Review
Chemistry, Multidisciplinary
Bolong Zhang, Guanpeng Lyu, Elaine A. Kelly, Rachel C. Evans
Summary: This article introduces the potential of Förster resonance energy transfer (FRET) in enhancing the performance of luminescent solar concentrators (LSCs), including key criteria and interactions with the host material. The authors aim to showcase the potential of FRET-LSCs in both conventional solar harvesting and emerging LSC-inspired technologies, while also encouraging diverse participation from researchers by addressing unanswered questions in this field.
Article
Polymer Science
Yan Yu, Xitian Li, Yongjie Yuan, Hailiang Zhang
Summary: This study developed a side-chain alternative copolymer P(DMPNI-alt-TPE) with large Stokes shift and tunable properties, showing potential prospects in the application of fluorescence materials.
Article
Energy & Fuels
Ting Wang, Xunchang Wang, Renqiang Yang, Chaoxu Li
Summary: This review discusses the successful application of Forster resonance energy transfer (FRET) in promoting the efficiencies of ternary blend organic solar cells (TOSCs), highlighting the diverse framework structures of FRET pairs and the role of FRET theory in the photoconversion process, including exciton harvesting, exciton diffusion, and charge generation. Existing challenges and future research directions of FRET applications in TOSCs are also proposed.
Article
Chemistry, Applied
Jifu Sun, Mingmei Shu, Ningyuan Wang, Qun Wang, Huaiman Cao, Xue Zhang, Bo Wang, Jianzhang Zhao
Summary: The intensity of host-guest interactions plays a crucial role in FRET/TTET processes based on fluorophore-functionalized pillararenes. Changing the polarity and size of solvents affects the strength of the interactions, leading to variations in the efficiency of FRET/TTET.
Article
Chemistry, Analytical
Roberto F. Delgadillo, Katie A. Carnes, Kathia Zaleta-Rivera, Omar Olmos, Lawrence J. Parkhurst
Summary: The time-resolved donor-detected Forster resonance energy transfer (trDDFRET) allows observation of molecular interactions within 10-100 angstrom region, while the time-resolved acceptor-detected FRET (trADFRET) can observe longer-range interactions. A new methodology based on trADFRET, namely FLIM-trADFRET, has been proposed to observe biological machinery in the range of 100-300 A in vivo. Proof of concept was demonstrated with a set of well-defined DNA scaffolds to evaluate this new methodology.
ANALYTICAL CHEMISTRY
(2021)
Article
Chemistry, Multidisciplinary
Yang Zou, Saran Long, Tao Xiong, Xueze Zhao, Wen Sun, Jianjun Du, Jiangli Fan, Xiaojun Peng
Summary: In this study, a new strategy using the single-molecule Forster resonance energy transfer (smFRET) mechanism was developed to transfer part of the fluorescent energy into heat for combined photodynamic and photothermal therapy. This approach not only improves treatment efficiency but also enables fluorescence imaging. The strategy allows for a combination treatment outcome at relatively low concentrations and light doses.
ACS CENTRAL SCIENCE
(2021)
Review
Plant Sciences
Zhikun Duan, Kaiwen Li, Wenwen Duan, Junli Zhang, Jingjing Xing
Summary: This article highlights the importance of using FRET technology to study interactions of plant membrane proteins, providing an overview of its applications in quantifying dynamic interactions and assemblies, as well as sensors for quantifying signaling molecule homeostasis and kinase activity. The recent applications of advanced FRET sensors in probing membrane protein interactions, stoichiometry, and clustering have shed light on the complex biological functions of membrane proteins in living plant cells.
JOURNAL OF EXPERIMENTAL BOTANY
(2022)
Article
Materials Science, Multidisciplinary
Sameer Al-Bati, Mohammad Hafizuddin Hj Jumali, Khatatbeh Ibtehaj, Bandar Ali Al-Asbahi, Chi Chin Yap
Summary: The study comprehensively investigated the energy transfer mechanism between different polymers and calculated various energy transfer parameters to confirm the occurrence of energy transfer and its influencing factors. Spectral analysis and parameter calculation provided insights into the energy transfer process in the three binary blends studied.
Article
Chemistry, Multidisciplinary
Jacob R. Pope, Rachel L. Johnson, W. David Jamieson, Harley L. Worthy, Senthilkumar Kailasam, Rochelle D. Ahmed, Ismail Taban, Husam Sabah Auhim, Daniel W. Watkins, Pierre J. Rizkallah, Oliver K. Castell, D. Dafydd Jones
Summary: Fluorescent proteins (FPs) are often used in pairs to monitor biomolecular events. GFP homodimers are brighter than monomers, while heterodimers typically have lower FRET efficiency than predicted.
Article
Chemistry, Physical
Claudia L. Gomez-Flores, Denis Maag, Mayukh Kansari, Van-Quan Vuong, Stephan Irle, Frauke Graeter, Tomas Kubar, Marcus Elstner
Summary: This paper investigates the generation of free energy surfaces in complex reactions using the semiempirical method DFTB and improves its accuracy by developing a specific reaction parametrization (SRP). Through the implementation of an artificial neural network (ANN), the authors successfully generate highly accurate free energy surfaces for thiol-disulfide exchange in two molecular complexes.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Chemistry, Physical
Gang Seob Jung, Stephan Irle, Bobby G. Sumpter
Summary: Graphene, as a two-dimensional carbon material, has attracted much attention due to its mechanical properties and failure mechanism. This study utilizes molecular dynamics simulations with density functional based tight binding to investigate the initiation of failure in pristine graphene. The findings suggest that a single threshold value for bond order or bond length is insufficient to determine the failure of pristine graphene, and instead, the collective behavior of local atomic groups plays a crucial role in fracture initiation.
Article
Chemistry, Physical
Akihiro Kimura, Hirotaka Kitoh-Nishioka, Toshimichi Aota, Tasuku Hamaguchi, Koji Yonekura, Keisuke Kawakami, Kyoko Shinzawa-Itoh, Natsuko Inoue-Kashino, Kentaro Ifuku, Eiki Yamashita, Yasuhiro Kashino, Shigeru Itoh
Summary: A theoretical model of the far-red-light-adapted photosystem I reaction center in a cyanobacterium, Acaryochloris marina, was constructed based on the exciton theory and compared with the traditional photosynthetic system.
JOURNAL OF PHYSICAL CHEMISTRY B
(2022)
Article
Chemistry, Physical
Karthik Ganeshan, Rabi Khanal, Murali Gopal Muraleedharan, Matti Hellstrom, Paul R. C. Kent, Stephan Irle, Adri C. T. van Duin
Summary: Protons and water transport in confined interlayer spaces of 2D materials are influenced by nuclear quantum effects, which need to be considered for accurate simulations and predictions.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2022)
Article
Multidisciplinary Sciences
Mikhail Ali Hameedi, Erica T. Prates, Michael R. Garvin, Irimpan Mathews, B. Kirtley Amos, Omar Demerdash, Mark Bechthold, Mamta Iyer, Simin Rahighi, Daniel W. Kneller, Andrey Kovalevsky, Stephan Irle, Van-Quan Vuong, Julie C. Mitchell, Audrey Labbe, Stephanie Galanie, Soichi Wakatsuki, Daniel Jacobson
Summary: The authors report on the crystallographic and computational studies that explain how SARS-CoV-2 3CLpro cleaves NF-kappa B Essential Modulator, a host protein, in addition to its viral substrates. They discuss the association between this cleavage and the high adaptability of SARS-CoV-2 in humans. The study provides insights into the binding mechanism between 3CLpro and NEMO, as well as the role of key binding residues in the fitness of SARS-CoV-2.
NATURE COMMUNICATIONS
(2022)
Article
Materials Science, Multidisciplinary
Massimiliano Lupo Pasini, Gang Seob Jung, Stephan Irle
Summary: We developed HydraGNN, a PyTorch-based architecture, which utilizes graph convolutional neural networks (GCNNs) to predict the formation energy and bulk modulus of solid solution alloy models with different atomic crystal structures and relaxed volumes. The GCNN surrogate model was trained using a dataset for nickel-niobium (NiNb) generated by the embedded atom model (EAM) empirical interatomic potential for demonstration purposes. The dataset was generated by calculating the formation energy and bulk modulus for optimized geometries starting from initial body-centered cubic (BCC), face-centered cubic (FCC), and hexagonal compact packed (HCP) crystal structures, covering the possible compositional range for each structure type. Numerical results demonstrate that the GCNN model effectively predicts the formation energy and bulk modulus based on the optimized crystal structure, relaxed volume, and configurational entropy of the solid solution alloy models.
COMPUTATIONAL MATERIALS SCIENCE
(2023)
Article
Chemistry, Physical
Van-Quan Vuong, Caterina Cevallos, Ben Hourahine, Balint Aradi, Jacek Jakowski, Stephan Irle, Cristopher Camacho
Summary: We accelerated the density-functional tight-binding (DFTB) method on single and multiple graphical processing units (GPUs) using the MAGMA linear algebra library. Our implementation addressed two major computational bottlenecks of DFTB ground-state calculations: the Hamiltonian matrix diagonalization and the density matrix construction. The code was tested on the SUMMIT IBM Power9 supercomputer and an in-house Intel Xeon computer, showing good performance and parallel scalability for carbon nanotubes, covalent organic frameworks, and water clusters.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Rabi Khanal, Stephan Irle
Summary: We studied the electron transport within the MXene layers as a function of composition and found a linear relationship between current and voltage at lower potentials in all MXene compositions, indicating their metallic character. However, the conductivity varies among different compositions, with MXenes without surface terminations exhibiting higher conductivity compared to MXenes with surface functionalization. The conductivity also changes with the ratio of -O and -OH on the MXene surface. The surface composition-dependent conductivity of MXenes provides a way to enhance the pseudocapacitive performance.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Chemistry, Physical
Hiroya Nakata, Hirotaka Kitoh-Nishioka, Wakana Sakai, Cheol Ho Choi
Summary: A multiscale scheme (MLMS: Multi-Level Multi -Scale) has been proposed to predict the ion mobility (mu) of amorphous organic semiconductors. It has been successfully applied to predict the hole mobility of 14 organic systems. The study reveals an inverse relationship between mu and reorganization energy due to local polaronic distortions, as well as a moderate inverse correlation between mu and distribution of site energy change, which represents the effects of geometric flexibility.
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
(2023)
Article
Physics, Multidisciplinary
Hirofumi Yanagisawa, Markus Bohn, Hirotaka Kitoh-Nishioka, Florian Goschin, Matthias F. Kling
Summary: Single-molecule electron sources, driven by constant electric fields and about 1 nm in size, exhibit unique emission patterns like crosses or two-leaf patterns. By illuminating these sources with femtosecond light pulses, we discovered highly modulated emission patterns originating from single-molecule molecular orbitals, solving a longstanding question. Our simulations achieved subnanometric optical modulation of an electron source through variations in the molecular orbitals of single molecules.
PHYSICAL REVIEW LETTERS
(2023)
Article
Chemistry, Multidisciplinary
Andrew E. Blanchard, Debsindhu Bhowmik, Zachary Fox, John Gounley, Jens Glaser, Belinda S. Akpa, Stephan Irle
Summary: The vast chemical space requires computational approaches to automate molecular sequence design for drug discovery. Genetic algorithms and masked language models are used to generate mutations in known chemical structures. The adaptive strategy of training the language model on new generations of molecules selected for target properties improves fitness optimization compared to the fixed pre-trained model. The application of language models to molecular design tasks is empowered by the adaptive strategy and demonstrates significant improvements in fitness optimization.
JOURNAL OF CHEMINFORMATICS
(2023)
Article
Computer Science, Artificial Intelligence
Gang Seob Jung, Hunjoo Myung, Stephan Irle
Summary: Understanding the mechanics and failure of materials at the nanoscale is crucial. Neural network potentials (NNPs) have emerged as a promising tool for accurate and efficient modeling. However, their application to deformation and failure processes in materials is still limited.
MACHINE LEARNING-SCIENCE AND TECHNOLOGY
(2023)
Meeting Abstract
Chemistry, Physical
Robert K. Szilagyi, Nicholas P. Stadie, Stephan Irle, Hirotomo Nishihara
Proceedings Paper
Computer Science, Artificial Intelligence
Andrew E. Blanchard, Pei Zhang, Debsindhu Bhowmik, Kshitij Mehta, John Gounley, Samuel Temple Reeve, Stephan Irle, Massimiliano Lupo Pasini
Summary: This study presents a workflow for accelerating the design of molecular compounds by combining approximate quantum chemical methods, a neural network surrogate model for chemical property prediction, and a language model for molecule generation. The workflow enables faster searching of chemical space and the generation of optimized molecules, showing potential for a wide range of design problems.
ACCELERATING SCIENCE AND ENGINEERING DISCOVERIES THROUGH INTEGRATED RESEARCH INFRASTRUCTURE FOR EXPERIMENT, BIG DATA, MODELING AND SIMULATION, SMC 202
(2022)
Article
Chemistry, Multidisciplinary
Rabi Khanal, Stephan Irle
Summary: Quantum chemical molecular dynamics simulations were used to investigate the impact of K+, Na+, and Mg2+ ions on the static and dynamic structure of bulk water in aqueous solutions. The study found that salt ions generally slow down the dynamic decay of pair correlations in the water solvation sphere, with differences observed between different cation types.