Simultaneous adsorption of cationic and anionic dyes using a novel multifunctional mesoporous silica

In the present work a multifunctional nanoadsorbent was synthesized via a well-designed stepwise route, led to the grafting of an amine group on the interior and acidic sites on the exterior of bimodal mesoporous silica nanoparticles (UVM-7). First, amine and thiol groups were grafted on the interior and exterior pores of silica through co-condensation and post synthesis treatment, respectively. Then, the oxidation of thiol on UVM-7 caused to create sulfonic acid and the subsequent template extraction was carried out to obtain the NH2/UVM-7/SO3H. The results of XRD, the nitrogen sorption, SEM, TEM, FT-IR and elemental analysis revealed the presence of both types of functional groups on UVM-7. Then, simultaneous adsorption of anionic and cationic dyes (Methylene Blue [MB] and Direct Red 23 [Dr]) using NH2/UVM-7/SO3H was investigated. UV-Vis spectrophotometry was utilized for the determination of dyes in single and binary solutions. Langmuir and Freundlich models were used for the fitting of obtained experimental adsorption data and the constants of both isotherms were calculated for MB and Dr. Morover, the calculation of thermodynamic parameters revealed that the adsorption of MB and Dr on NH2/UVM-7/SO3H was endothermic and spontaneous.

Automated Detection and Classification of Defective and Abnormal Dies in Wafer Images

This article presents an automated vision-based algorithm for the die-scale inspection of wafer images captured using scanning acoustic tomography (SAT). This algorithm can find defective and abnormal die-scale patterns, and produce a wafer map to visualize the distribution of defects and anomalies on the wafer. The main procedures include standard template extraction, die detection through template matching, pattern candidate prediction through clustering, and pattern classification through deep learning. To conduct the template matching, we first introduce a two-step method to obtain a standard template from the original SAT image. Subsequently, a majority of the die patterns are detected through template matching. Thereafter, the columns and rows arranged from the detected dies are predicted using a clustering method; thus, an initial wafer map is produced. This map is composed of detected die patterns and predicted pattern candidates. In the final phase of the proposed algorithm, we implement a deep learning-based model to determine defective and abnormal patterns in the wafer map. The experimental results verified the effectiveness and efficiency of our proposed algorithm. In conclusion, the proposed method performs well in identifying defective and abnormal die patterns, and produces a wafer map that presents important information for solving wafer fabrication issues.

Artificial Applicability Labels for Improving Policies in Retrosynthesis Prediction

Automated retrosynthetic planning algorithms are a research area of increased importance. Automated reaction template extraction from large datasets in conjunction with neural network enhanced tree search algorithms can find plausible routes to target compounds in seconds. However, the current way of training the neural networks to predict suitable templates for a given target product, leads to many predictions which are not applicable <i>in silico</i>. Most templates in the top-50 suggested templates can’t be applied to the target molecule to perform the virtual reaction. Here we describe how to generate data and train a neural network policy that predicts if templates are applicable or not. First, we generate a massive training dataset by applying each retrosynthetic template to each product from our reaction database. Second, we train a neural network to near perfect prediction of the applicability labels on a held-out test set. The trained network is then joined with a policy model trained to predict and prioritize templates using the labels from the original dataset. The combined model was found to outperform the policy model in a route-finding task using 1700 compounds from our internal drug discovery projects.

Artificial Applicability Labels for Improving Policies in Retrosynthesis Prediction

Automated retrosynthetic planning algorithms are a research area of increased importance. Automated reaction template extraction from large datasets in conjunction with neural network enhanced tree search algorithms can find plausible routes to target compounds in seconds. However, the current way of training the neural networks to predict suitable templates for a given target product, leads to many predictions which are not applicable <i>in silico</i>. Most templates in the top-50 suggested templates can’t be applied to the target molecule to perform the virtual reaction. Here we describe how to generate data and train a neural network policy that predicts if templates are applicable or not. First, we generate a massive training dataset by applying each retrosynthetic template to each product from our reaction database. Second, we train a neural network to near perfect prediction of the applicability labels on a held-out test set. The trained network is then joined with a policy model trained to predict and prioritize templates using the labels from the original dataset. The combined model was found to outperform the policy model in a route-finding task using 1700 compounds from our internal drug discovery projects.

RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application

There is a renewed interest in computer-aided synthesis planning, where the vast majority of approaches require the application of retrosynthetic reaction templates. Here, we introduce an open source Python wrapper for RDKit designed to provide consistent handling of stereochemical information in applying retrosynthetic transformations encoded as SMARTS strings. RDChiral is designed to enforce the introduction, destruction, retention, and inversion of tetrahedral centers as well as the cis/trans chirality of double bonds. We also introduce an open source implementation of a retrosynthetic template extraction algorithm to generate SMARTS patterns from atom-mapped reaction SMILES strings. In this manuscript, we describe the implementation of these two pieces of code and illustrate their use through many examples.<div><br></div><div>The two .json.gz files can be generated from the open source USPTO data available at https://figshare.com/articles/Chemical_reactions_from_US_patents_1976-Sep2016_/5104873 using the code contained in the rdchiral GitHub repository. They are placed here for convenience if you would prefer to copy them into the templates/data subfolder instead of creating them from the source .rsmi file.</div>

RDChiral: An RDKit Wrapper for Handling Stereochemistry in Retrosynthetic Template Extraction and Application

There is a renewed interest in computer-aided synthesis planning, where the vast majority of approaches require the application of retrosynthetic reaction templates. Here, we introduce an open source Python wrapper for RDKit designed to provide consistent handling of stereochemical information in applying retrosynthetic transformations encoded as SMARTS strings. RDChiral is designed to enforce the introduction, destruction, retention, and inversion of tetrahedral centers as well as the cis/trans chirality of double bonds. We also introduce an open source implementation of a retrosynthetic template extraction algorithm to generate SMARTS patterns from atom-mapped reaction SMILES strings. In this manuscript, we describe the implementation of these two pieces of code and illustrate their use through many examples.<div><br></div><div>The two .json.gz files can be generated from the open source USPTO data available at https://figshare.com/articles/Chemical_reactions_from_US_patents_1976-Sep2016_/5104873 using the code contained in the rdchiral GitHub repository. They are placed here for convenience if you would prefer to copy them into the templates/data subfolder instead of creating them from the source .rsmi file.</div>