Neural-based modeling adsorption capacity of metal organic framework materials with application in wastewater treatment.

We developed a computational-based model for simulating adsorption capacity of a novel layered double hydroxide (LDH) and metal organic framework (MOF) nanocomposite in separation of ions including Pb(II) and Cd(II) from aqueous solutions. The simulated adsorbent was a composite of UiO-66-(Zr)-(COOH)2 MOF grown onto the surface of functionalized Ni50-Co50-LDH sheets. This novel adsorbent showed high surface area for adsorption capacity, and was chosen to develop the model for study of ions removal using this adsorbent. A number of measured data was collected and used in the simulations via the artificial intelligence technique. Artificial neural network (ANN) technique was used for simulation of the data in which ion type and initial concentration of the ions in the feed was selected as the input variables to the neural network. The neural network was trained using the input data for simulation of the adsorption capacity. Two hidden layers with activation functions in form of linear and non-linear were designed for the construction of artificial neural network. The model's training and validation revealed high accuracy with statistical parameters of R2 equal to 0.99 for the fitting data. The trained ANN modeling showed that increasing the initial content of Pb(II) and Cd(II) ions led to a significant increment in the adsorption capacity (Qe) and Cd(II) had higher adsorption due to its strong interaction with the adsorbent surface. The neural model indicated superior predictive capability in simulation of the obtained data for removal of Pb(II) and Cd(II) from an aqueous solution.

The use of multivariate curve resolution methods to improve the analysis of muramic acid as bacterial marker using gas chromatography-mass spectrometry: an alternative method to gas chromatography-tandem mass spectrometry.

In analysis of muramic acid (MA) as bacterial marker, two dominant disturbing factors lead the researchers to use gas chromatography-tandem mass spectrometry (GC-MS/MS) technique instead of gas chromatography-mass spectrometry (GC-MS). These factors are the trace concentration of MA and fundamental disturbance of base line mass channels in GC-MS technique. This study aimed to utilize multivariate curve resolution (MCR) methods combined with GC-MS to improve the analysis of MA. First, the background and noise in GC-MS analysis were corrected and reduced using MCR methods. In addition, the MA overlapped peaks were resolved to its pure chromatographic and mass spectral profiles. Then the two-way response of each component was reconstructed by the outer product of the pure chromatographic and mass spectral profiles. The overall volume integration (OVI) method was used for quantitative determination. The MA peak area was decreased dramatically after the background correction and noise reduction. The findings severely ratify the appropriateness of using MCR techniques combined with GC-MS analysis as a simple, fast and inexpensive method for the analysis of MA in complex mixtures. The proposed method may be considered as an alternative method to GC-MS/MS for thorough analysis of the bacterial marker.