Article
Chemistry, Multidisciplinary
Zafer Acar, Phu Nguyen, Kah Chun Lau
Summary: Ionic liquids have great potential for energy storage and conversion devices, but their practical application is limited due to unfavorable melting points. Accurate prediction of the melting points is important for fine tuning their properties. A deep-learning model was used to predict the melting points of various ionic liquids, achieving high accuracy and providing useful design rules for tuning their melting points.
APPLIED SCIENCES-BASEL
(2022)
Article
Chemistry, Physical
Peyvand Valeh-e-Sheyda, Pouria Heidarian, Abbas Rezvani
Summary: This study presents a comprehensive database of CO2 equilibrium solubility measurements in imidazolium-based ionic liquids (ILs), and develops a descriptor-based model using machine learning methods. The suggested model, based on feed-forward neural network, demonstrates reliable prediction performance for CO2 equilibrium solubility in imidazolium-based ILs.
JOURNAL OF MOLECULAR LIQUIDS
(2022)
Article
Biochemistry & Molecular Biology
Dhruve Kumar Mital, Paul Nancarrow, Samira Zeinab, Nabil Abdel Jabbar, Taleb Hassan Ibrahim, Mustafa I. Khamis, Alnoman Taha
Summary: In recent years, several group contribution method (GCM) models have been developed for the prediction of ionic liquid (IL) properties, with challenges including reliance on different datasets and limited IL range. This study focuses on two diverse GCMs for estimating IL melting points, refining one model to improve performance. The comprehensive database compiled here aids in targeted design of ILs for materials and energy applications.
Article
Chemistry, Physical
Mariam Abdullah, Kallidanthiyil Chellappan Lethesh, Ahmer A. B. Baloch, Musbaudeen O. Bamgbopa
Summary: Predicting the ionic conductivity of ionic liquids using machine learning techniques is important and the accuracy of the predictions depends on the types of features used. This study uses a more extensive and diverse dataset to show that using structural features alone outperforms using molecular features alone, and combining both types of features yields the best prediction results.
JOURNAL OF MOLECULAR LIQUIDS
(2022)
Article
Chemistry, Physical
Jeremiasz Pilarz, Ilya Polishuk, Miroslaw Chorazewski
Summary: Ionic liquids have great potential as new hydraulic oils and high-pressure heat transfer media. This study proposes a simple neural network model for predicting the speed of sound and adiabatic compressibility data in ionic liquids, and compares its results with the predictions of the CP-PC-SAFT Equation of State.
JOURNAL OF MOLECULAR LIQUIDS
(2022)
Article
Pharmacology & Pharmacy
Bing Shao, Youyang Qu, Wei Zhang, Haihe Zhan, Zerong Li, Xingyu Han, Mengchao Ma, Zhimin Du
Summary: This study proposes a neural network model based on seven variables to accurately predict the development of tremors in patients with nephrotic syndrome following tacrolimus. The model shows high accuracy, sensitivity, and specificity in both the training and validation sets, making it beneficial for the treatment of nephrotic syndrome patients.
FRONTIERS IN PHARMACOLOGY
(2022)
Article
Chemistry, Physical
Tim Peppel, Martin Koeckerling
Summary: A series of new low-melting triply charged homoleptic Cr(III)-based ionic liquids and sixteen new Reineckate related salts with large imidazolium cations are reported. The compounds with [Cr(NCS)(6)](3-) anion have relatively low melting points and are stable up to 200 K above their melting points, while those with [Cr(NCS)(4)L-2](-) anion start to decompose at the melting point.
Article
Chemistry, Physical
Cettina Bottari, Sara Catalini, Paolo Foggi, Ines Mancini, Andrea Mele, Diego Romano Perinelli, Alessandro Paciaroni, Alessandro Gessini, Claudio Masciovecchio, Barbara Rossi
Summary: Hydrated ionic liquids (ILs) have been found to enhance the structural stability of DNA. The cations and anions of ILs strongly interact with the DNA structure, affecting the melting process but not perturbing the pre-melting transition. The dominant interaction occurs between the imidazolium cation and the guanine and thymine bases in DNA structure.
JOURNAL OF MOLECULAR LIQUIDS
(2021)
Article
Thermodynamics
Alexandre S. Zimmermann, Silvana Mattedi
Summary: Ionic liquids have attracted significant interest due to their properties such as low vapor pressure, thermal and chemical stability, non-flammability, and the ability to be designed for specific purposes. In this study, neural networks were trained and used to predict the viscosity of binary mixtures of ammonium-based ionic liquids and water. The input variables included temperature, ionic liquid mass fraction, and the amount of each group used to describe the ionic liquid structures. The study investigated the influence of hidden neuron numbers, the addition of an extra hidden layer, and tested 13 training algorithms. The results showed that neural networks can successfully predict viscosity data, but careful selection of architecture and training algorithm is necessary.
FLUID PHASE EQUILIBRIA
(2022)
Article
Multidisciplinary Sciences
Pengshun Li, Jiarui Chang, Yi Zhang, Yi Zhang
Summary: The study proposed a multi-zone order demand prediction model to effectively predict taxi order demand in different zones at city-scale. This model includes two steps: zone division and multi-zone order prediction, and utilizes multiple methods for prediction.
Article
Chemistry, Multidisciplinary
Youqi Li, Xiaopeng Chen, Linlin Wang, Xiaojie Wei, Minting Nong, Weijian Nong, Jiezhen Liang
Summary: The vapor-liquid equilibrium and thermodynamic properties of a turpentine + rosin system were studied using headspace gas chromatography and the COSMO-RS model. The results showed that the activity coefficients of all components of turpentine deviated positively from Raoult's law except for longifolene. The mixing of turpentine and rosin was found to be endothermic.
Article
Horticulture
Liyun Gong, Miao Yu, Vassilis Cutsuridis, Stefanos Kollias, Simon Pearson
Summary: In this study, a novel methodology for predicting greenhouse tomato yield is proposed, which combines the Tomgro model and the CNN-RNN model. Different fusion approaches are used to merge the prediction results of these models, and the neural network fusion approach yields the most accurate tomato predictions.
Article
Chemistry, Multidisciplinary
Yinglong Wang, Tianxiong Liu, Zihao Dong, Wenguang Zhu, Yusen Chen, Mengjin Zhou, Peizhe Cui, Zhaoyou Zhu
Summary: This study establishes a quantitative structure-property relationship (QSPR) model of ionic liquids (ILs) using molecular descriptors (MD), molecular identifiers (MI), and their combinations. The QSPR model, built by coupling deep neural network (DNN) and random forest (RF), shows the best performance when using MI to represent ILs and DNN. The Shapley additive explanation (SHAP) method is applied to analyze features and obtain valuable molecular structure information for prediction. Different MD and MI contribute differently in the prediction of H2S solubility and can correctly identify the impact of environmental factors (temperature and pressure). Additionally, the influence of different carbon chain lengths on the solubility of H2S is studied by calculating the electrostatic potential (ESP) between H2S and ILs.
ACS SUSTAINABLE CHEMISTRY & ENGINEERING
(2023)
Correction
Chemistry, Physical
Brooks D. Rabideau, Mohammad Soltani, Rome A. Parker, Benjamin Siu, E. Alan Salter, Andrzej Wierzbicki, Kevin N. West, James H. Davis Jr
Summary: The melting point of selected ionic liquids can be tuned by adjusting the dipole moment of the cation.
PHYSICAL CHEMISTRY CHEMICAL PHYSICS
(2023)
Article
Automation & Control Systems
Yundi Chu, Juntao Fei, Shixi Hou
Summary: This paper presents a dual-loop recursive fuzzy neural network (DRFNN) based adaptive controller for a type of nonlinear dynamic systems. Compared to conventional FNN, this controller allows arbitrary parameter setting and achieves higher approximation accuracy and learning speed.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Editorial Material
Computer Science, Artificial Intelligence
Simone Scardapane, Claudio Gallicchio, Alessio Micheli, Miguel C. Soriano
COGNITIVE COMPUTATION
(2023)
Article
Computer Science, Artificial Intelligence
Claudio Gallicchio, Alessio Micheli
Summary: This paper delves into the analysis of untrained RNNs by examining the quality of the recurrent dynamics developed by the layers of deep RC neural networks. The experiments demonstrate that depth, as an architectural factor, has a natural effect on the quality of RNN dynamics and that the interplay between depth and RC scaling hyper-parameters is crucial in designing rich untrained recurrent neural systems.
NEURAL COMPUTING & APPLICATIONS
(2022)
Editorial Material
Computer Science, Artificial Intelligence
Gouhei Tanaka, Claudio Gallicchio, Alessio Micheli, Juan-Pablo Ortega, Akira Hirose
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Luca Oneto, Nicolo Navarin, Battista Biggio, Federico Errica, Alessio Micheli, Franco Scarselli, Monica Bianchini, Luca Demetrio, Pietro Bongini, Armando Tacchella, Alessandro Sperduti
Summary: This paper discusses the challenges faced by Machine Learning in the digitization and datification of people's daily life, and presents works focused on trustworthy, automatic, and guaranteed learning on graphs.
Article
Computer Science, Artificial Intelligence
Alessio Micheli, Domenico Tortorella
Summary: This paper presents DynGESN, a reservoir computing model for efficient processing of discrete-time dynamic temporal graphs. Experimental results show that DynGESN achieves comparable accuracy to TGKs with similar computational complexity, while offering better space and time requirements for large-scale data. Moreover, DynGESN outperforms TGNs in terms of efficiency, with up to ten times improvement in inference and training time.
Article
Agricultural Engineering
Jose Gonzalez-Rivera, Beatrice Campanella, Elena Pulidori, Emilia Bramanti, Maria Rosaria Tine, Luca Bernazzani, Massimo Onor, Paolo Barberi, Celia Duce, Carlo Ferrari
Summary: Novel green extraction method (US-IMWAE) using simultaneous ultrasound and microwave irradiation allows for full exploitation of aromatic herbs while minimizing energy consumption and extraction cost. This method has successfully yielded essential oils with appealing composition profiles for cosmetic and medical industries, as well as valuable solid residues for alternative fuel production.
INDUSTRIAL CROPS AND PRODUCTS
(2023)
Article
Chemistry, Physical
Elena Pulidori, Simone Micalizzi, Nikos Koutsomarkos, Emilia Bramanti, Maria Rosaria Tine, Giovanni Vozzi, Carmelo De Maria, Maria Chatzinikolaidou, Celia Duce
Summary: This study investigates the structure and correlations between structure, mechanical, and biological properties of gelatin-based materials. Gelatin electro-spun materials have higher content of ordered structure compared to gelatin casted films, which may be attributed to the random coil - alpha-helix transition during electrospinning. The presence of cross linker (GPTMS) decreases the ordered structures in gelatin casted films, while it does not affect the percentage of ordered structure in electrospun samples. The presence of keratin in gelatin/keratin electrospun biomaterials decreases alpha-helix content and promotes the conversion from antiparallel to parallel beta-sheet.
JOURNAL OF MOLECULAR STRUCTURE
(2023)
Article
Multidisciplinary Sciences
Ophelie Ranquet, Celia Duce, Emilia Bramanti, Patrick Dietemann, Ilaria Bonaduce, Norbert Willenbacher
Summary: The authors use a combination of egg yolk and two pigments to investigate how different distribution of proteinaceous binder can control the flow behavior, drying kinetics, and chemistry of oil paints. This study demonstrates that adjusting the distribution of proteinaceous binder can enhance the stiffening effect and prevent undesired absorption of humidity, improving the brushability and preserving invaluable artworks.
NATURE COMMUNICATIONS
(2023)
Article
Chemistry, Physical
Chiara Pelosi, Jose Gonzalez-Rivera, Maria Rosaria Tine, Gianluca Ciancaleoni, Luca Bernazzani, Celia Duce
Summary: This article presents a physicochemical characterization of type-II DESs/water mixtures using various techniques to analyze and evaluate the samples from both structural and application perspectives.
JOURNAL OF MOLECULAR LIQUIDS
(2023)
Proceedings Paper
Computer Science, Artificial Intelligence
Daniele Castellana, Federico Errica, Davide Bacciu, Alessio Micheli
Summary: This paper introduces the applications of the Contextual Graph Markov Model (CGMM) and the Infinite Contextual Graph Markov Model (ICGMM), which can adapt the complexity of each layer based on the underlying data distribution. ICGMM achieves good performance in graph classification tasks.
INTERNATIONAL CONFERENCE ON MACHINE LEARNING, VOL 162
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Domenico Tortorella, Claudio Gallicchio, Alessio Micheli
Summary: This paper introduces a model called Graph Echo State Networks (GESN) for efficient and effective processing of graphs. By converging to a fixed point of a dynamical system, this model computes graph embeddings and allows easier parameter selection and better quality reservoirs through more accurate bounds.
2022 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
(2022)
Proceedings Paper
Computer Science, Artificial Intelligence
Domenico Tortorella, Claudio Gallicchio, Alessio Micheli
Summary: This paper presents an in-depth theoretical analysis of asymptotic dynamics in deep ESNs and provides a more accurate condition for the ESP. The study finds that structuring reservoir layers in decreasing contractivity offers the best design choice. The results of this paper can potentially be applied to the design of fully-trained RNNs.
ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2022, PT III
(2022)
Article
Thermodynamics
Silvia Pizzimenti, Luca Bernazzani, Maria Rosaria Tine, Celia Duce, Ilaria Bonaduce
Summary: This study investigated the effect of amorphous carbon black on the mechanism and speed of autoxidation of a polyunsaturated oil, as well as the effect of the addition of aluminium stearate and zinc stearate. The results showed that amorphous carbon black has an antioxidant effect and the addition of aluminium and zinc stearates accelerates the formation of peroxides.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2022)
Article
Thermodynamics
Elena Pulidori, Anna Lluveras-Tenorio, Rita Carosi, Luca Bernazzani, Celia Duce, Stefano Pagnotta, Marco Lezzerini, Germana Barone, Paolo Mazzoleni, Maria Rosaria Tine
Summary: This study conducted thermal analysis and structural characterization of a set of geomaterials for the synthesis of geopolymers designed for the conservation of Cultural Heritage buildings, especially in seismic hazard zones. Statistical treatment of the data highlighted the direct relation between thermal data and material composition, aiding in the selection of optimal materials.
JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY
(2022)
Article
Chemistry, Multidisciplinary
Xuemei Liu, Chaonan Cui, Shuoshuo Wei, Jinyu Han, Xinli Zhu, Qingfeng Ge, Hua Wang
Summary: This study presents a new strategy for designing efficient photocatalysts that can convert CO2 into hydrocarbons by utilizing synergistic catalytic sites. The findings provide a solution for the selective photocatalytic reduction of CO2 to CH4.
Article
Chemistry, Multidisciplinary
Chengxian Hu, Dan Wang, Lu Wang, Ying Fu, Zhengyin Du
Summary: A novel one-pot, three-component reaction conducted under electrochemical conditions was studied. The reaction involved 2-aminothiophenols, aldehydes, and malononitrile, using TBABF4 as an electrolyte and CuI as a catalyst. The proposed reaction mechanism suggested that CuI served as an electron relay. This method offers simplified operation, high atom economy, and mild reaction conditions.
Article
Chemistry, Multidisciplinary
Zhi Yang, Yu Chen, Linxi Wan, Yuxiao Li, Dan Chen, Jianlin Tao, Pei Tang, Fen-Er Chen
Summary: A highly enantioselective method for the complete hydrogenation of pyrimidinium salts using Ir/(S,S)-f-Binaphane complex as the catalyst was developed. This method provides easy access to fully saturated chiral hexahydropyrimidines, which are prevalent in many bioactive molecules. The reactions exhibit high yields and enantioselectivities under mild reaction conditions without additives. Successful application of this methodology in a continuous flow fashion further extends its practical utility.
Article
Chemistry, Multidisciplinary
Tina Jeoh, Jennifer Danger Nill, Wujun Zhao, Sankar Raju Narayanasamy, Liang Chen, Hoi-Ying N. Holman
Summary: In this study, the enzymatic hydrolysis of cellulose was investigated using real-time infrared spectromicroscopy. The spatial heterogeneity of cellulose was found to impact the hydrolysis kinetics. Hydration affected cellulose ordering, and Cel7A preferentially removed less extensively hydrogen bonded cellulose.
Article
Chemistry, Multidisciplinary
Tiphaine Richard, Walid Abdallah, Xavier Trivelli, Mathieu Sauthier, Clement Dumont
Summary: An effective method of grafting functionalities onto lignin based on glycerol carbonate has been developed using an efficient nickel-catalysed telomerisation reaction. This method allows lignin to have new reactive functions and reduces the glass transition temperatures of modified lignins, thereby expanding the application range of lignin-based resins.
Article
Chemistry, Multidisciplinary
Jing Qi, Xiyan Wang, Gan Wang, Srinivas Reddy Dubbaka, Patrick ONeill, Hwee Ting Ang, Jie Wu
Summary: This study presents a green and environmentally friendly approach for the synthesis of imides using electrocatalytic oxidation with H2O as the oxygen source. The method eliminates the need for toxic or expensive oxidants and achieves high yields under mild reaction conditions. It shows broad substrate compatibility and potential for industrial applications.
Article
Chemistry, Multidisciplinary
Babasaheb Sopan Gore, Lin-Wei Pan, Jun-Hao Lin, Yi-Chi Luo, Jeh-Jeng Wang
Summary: Here, we report a visible light-promoted intramolecular radical cascade reaction for the construction of fluorenol and naphthalene-fused cyclopropyl carbaldehyde derivatives. This method offers mild reaction conditions, a broad substrate scope, excellent step efficiency, and scalability, without the need for external chemical oxidants. The novelty of this protocol was demonstrated by synthesizing chrysene analogs and performing late-stage functionalizations.
Article
Chemistry, Multidisciplinary
Juho Antti Sirvio, Idamaria Romakkaniemi, Juha Ahola, Svitlana Filonenko, Juha P. Heiskanen, Ari Ammala
Summary: This article discusses the method of using supramolecular interaction between an aromatic hydrogen bond donor and lignin to achieve rapid delignification of softwood at low temperatures.
Article
Chemistry, Multidisciplinary
Yunyan Meng, Chunxiang Pan, Na Liu, Hongjiang Li, Zixiu Liu, Yao Deng, Zixiang Wei, Jianbin Xu, Baomin Fan
Summary: A novel visible light-driven synthesis method for 2,3-diamines has been developed, which has mild conditions, avoids the use of metal reagents, and can synthesize diamines and diols in one pot.
Article
Chemistry, Multidisciplinary
Mingqing Huang, Haiyang Huang, Mengyao You, Xinxin Zhang, Longgen Sun, Chao Chen, Zhichao Mei, Ruchun Yang, Qiang Xiao
Summary: A direct air-oxidized strategy for the synthesis of benzo[b]phosphole oxides was developed in this study. Arylphosphine oxides were transformed into phosphinoyl radicals, which were further combined with various alkynes to achieve the desired products. DFT calculations revealed the mechanism of phosphinoyl radical formation.
Article
Chemistry, Multidisciplinary
Anwei Wang, Jiayin Huang, Chunsheng Zhao, Yu Fan, Junfeng Qian, Qun Chen, Mingyang He, Weiyou Zhou
Summary: This study demonstrates an innovative strategy for the aerobic oxidation of C(sp(3))-H bonds using gamma-valerolactone. By optimizing the reaction conditions and utilizing specific catalysts, efficient oxidation of C(sp(3))-H bonds is achieved with good chemoselectivity in certain cases.
Article
Chemistry, Multidisciplinary
Shun Li, Likai Tong, Zhijian Peng, Bo Zhang, Xiuli Fu
Summary: Sulfide compounds show promise as electrocatalysts for water splitting, but their performance is limited by factors such as limited active sites and hindered substance transport. This study successfully prepared a high-entropy sulfide (ZnCoMnFeAlMg)(9)S-8, which reduced grain size and increased specific surface area, enabling the realization of a dual-functional catalyst with multiple catalytic sites. High entropy also modulated the electronic properties of sulfides, reducing the potential energy barrier for hydrolysis. This research introduces a new approach for functionalizing high entropy nanomaterials and improves the performance of water splitting catalysts.