Quantitative Structure-Retention Relationship models were developed to identify phenolic compounds using a typical LC- system, with both UV and MS detection. A new chromatographic method was developed for the separation of fifty-two standard phenolic...
The predictive modeling of liquid chromatography methods can be an invaluable asset, potentially saving countless hours of labor while also reducing solvent consumption and waste. Tasks such as physicochemical screening and preliminary method screeni...
Chromatographic separation processes are most often modeled in the form of partial differential equations (PDEs) to describe the complex adsorption equilibria and kinetics. However, identifying parameters in such a model requires substantial computat...
Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and drug delivery. However, the variations in their components significantly impactfinal properties. Thi...
The monitoring of organic compounds in aquatic matrices poses challenges due to its complexity and time-intensive nature. To address these challenges, we introduce a novel approach utilizing a dual-channel mono (D) and comprehensive two-dimensional (...
The rapidly growing market of monoclonal antibodies (mAbs) within the biopharmaceutical industry has incentivised numerous works on the design of more efficient production processes. Protein A affinity chromatography is regarded as one of the best pr...
Artificial intelligence and machine learning techniques are increasingly used for different tasks related to method development in liquid chromatography. In this study, the possibilities of a reinforcement learning algorithm, more specifically a deep...
In recent years, comprehensive two-dimensional gas chromatography (GC × GC) has been gradually gaining prominence as a preferred method for the analysis of complex samples due to its higher peak capacity and resolution power compared to conventional ...
The retention time (RT) is a crucial source of data for liquid chromatography-mass spectrometry (LCMS). A model that can accurately predict the RT for each molecule would empower filtering candidates with similar spectra but differing RT in LCMS-base...
Numerical method is widely used for solving the mechanistic models of chromatography process, but it is time-consuming and hard to response in real-time. Physics-informed neural network (PINN) as an emerging technology combines the structure of neura...