Results 21 to 30 of about 15,492,277 (241)

Quantitative Structure–Property Relationship for the Retention Index of Volatile and Semi-Volatile Compounds of Coffee

Chemistry Proceedings, 2021
This study describes the development of a quantitative structure–property relationship to predict the retention indices of volatile and semi-volatile compounds identified in Arabica coffee samples from different geographical origins.
Cristian Rojas, Christian D. Alcívar León, Elizabeth Contreras Aguilar, Paola V. Mazón Ayala, Doménica Muñoz   +4 more
doaj   +1 more source

Quantitative structure-property relationship analysis of critical properties of individual organic compounds

Trudy Odesskogo Politehničeskogo Universiteta
This work presents the development and analysis of robust QSPR models for critical properties (pressure PC, temperature TC, and volume VC). The object of this study is a database consisting of 399 different organic compounds.
Єгор Костянтинович Пестерев, Людмила Миколаївна Огніченко, Анатолій Григорович Артеменко, Марія Євгенівна Кічьова, Наіль Ніязович Муратов, Кузьмін Віктор Євгенович   +5 more
doaj   +5 more sources

Enhanced Graph Isomorphism Network for Molecular ADMET Properties Prediction

IEEE Access, 2020
The evaluation of absorption, distribution, metabolism, exclusion, and toxicity (ADMET) properties plays a key role in a variety of domains including industrial chemicals, agrochemicals, cosmetics, environmental science, food chemistry, and particularly ...
Yuzhong Peng, Yanmei Lin, Xiao-Yuan Jing, Hao Zhang, Yiran Huang, Guang Sheng Luo   +5 more
doaj   +1 more source

Computation of Vertex-Based Topological Descriptors of Organometallic Monolayers of TM3C12S12

Journal of Mathematics, 2021
Topological descriptors are mathematical values related to chemical structures which are associated with different physicochemical properties. The use of topological descriptors has a great contribution in the field of quantitative structure-property ...
Dalal Alrowaili, Faraha Ashraf, Rifaqat Ali, Arsalan Shoukat, Aqila Shaheen, Mehran Azeem, Imran Siddique   +6 more
doaj   +1 more source

Application of quantitative structure property relationship to the design of high refractive index 193i resist [PDF]

, 2008
A robust quantitative structure property relationship (QSPR) model with five parameters has been developed from 126 organic compounds for the prediction of refractive index at 589 nm.
Conley, Willard, Whittaker, Andrew, Hill, David J. T., Hill, David, Conley, Willard E., Graeme George, Liu, Heping, George, Graeme, Blakey, Idriss, Whittaker, Andrew K.   +9 more
core   +1 more source

Design of novel high-performance fuels with artificial intelligence: Case study for spark-ignition engine applications

Applications in Energy and Combustion Science
The ever-increasing importance of both energy security and sustainability motivates the design of carbon-neutral petroleum replacements from renewable resources. Fuel candidates are conventionally selected from existing databases with limited scope. This
Zhuo Chen, Florian vom Lehn, Heinz Pitsch, Liming Cai   +3 more
doaj   +1 more source

Artificial Neural Network and Support Vector Regression Applied in Quantitative Structure-property Relationship Modelling of Solubility of Solid Solutes in Supercritical CO2 [PDF]

Kemija u Industriji, 2020
In this study, the solubility of 145 solid solutes in supercritical CO2 (scCO2) was correlated using computational intelligence techniques based on Quantitative Structure-Property Relationship (QSPR) models.
Mohammed Moussaoui, Maamar Laidi, Salah Hanini, Mohamed Hentabli   +3 more
doaj   +1 more source

Predicting H2S solubility in ionic liquids by the quantitative structure-property relationship method using S sigma-profile molecular descriptors

, 2016
Predicting hydrogen sulfide (H2S) solubility in ionic liquids (ILs) is vital for industrial gas desulphurization. In this work, the qualitative analysis of the influence of cations and anions on the H2S solubility in ILs has been conducted. The results
Zhang, Xiangping, Zhao, Yongsheng, Gao, Jubao, Zhang, Suojiang, Huang, Ying, Afzal, Raja Muhammad   +5 more
core   +1 more source

Predicting Surface Tensions and Electrical Conductivities for High Molecular Weight Ionic Liquid Propellants Using Quantitative Structure-Property Relationships (QSPR) [PDF]

, 2023
Quantitative Structure-Property Relationships (QSPR) take in existing experimental property data and output predicted properties using statistical analysis and linear regressions.
Steven D., Chambreau, Debasis, Sengupta, David A., Newsome, Ghanshyam L., Vaghjiani, Mark J., Wolf   +4 more
core   +1 more source

Descriptors-based machine-learning prediction of cetane number using quantitative structure–property relationship

Energy and AI
The physicochemical properties of liquid alternative fuels are important but difficult to measure/predict, especially when complex surrogate fuels are concerned.
Rodolfo S.M. Freitas, Xi Jiang
doaj   +1 more source