Cheometrics in the daily laboratory practice

Summary Developments in computing technology have drastically changed the way analyses are performed in the analytical research and routine laboratory. More complex calculations are possible on data measured by microcomputer controlled instruments. Moreover affordable hardware and software became available for the development and implementation of knowledge based systems, which are an important tool to tackle problems which can not be solved in an algorithmic way. As the extraction of relevant analytical information from large and complex data structures became more accessible, instrument development was further pushed in the direction of higher complexity of operation and data.Chemometrics and, recently, knowledge engineering, have produced tools to support the analyst at various stages during the analysis; method development, calibration, optimisation, signal processing and method validation.Apparently these tools are becoming the building blocks of intelligent analytical workstations arranged in the socalled intelligent laboratory.By a system analysis of the activities in an analytical laboratory, the functionalities of the intelligent laboratory are discussed. Furthermore, the current status of the intelligent laboratory is evaluated in terms of available systems, systems expected to become available in the near future and black spots are identified for which no tools are yet available, requiring more chemometrics input.This overview comprises aspects of method development, the validation of analytical results and the processing of these results into information. Examples are chosen from the intelligent chromatography and intelligent spectroscopy laboratory.     

Preparation and chromatographic properties of a multimodal chiral stationary phase based on phenyl-carbamate-propyl-beta-CD for HPLC

A phenylcarbamate derivative of 2-hydroxypropyl-beta-CD bonded stationary phase was prepared by a previously described method. Its enantiomeric recognition abilities were evaluated as chiral stationary phase (CSP) in normal, polar organic and RP conditions by HPLC. The relevant structural features of the prepared stationary phase which make it an effective chiral selector are discussed. This material seems to have an excellent enantioselectivity for a variety of racemic analytes in the three modes. Hence it can be considered a highly effective multimodal column. Retention factor (k), selectivity (alpha) and resolution (R(s)) were the chosen parameters to describe the column performance. Optimization of these separations was discussed in terms of mobile phase composition, flow rate and structural patterns of the injected analytes.     

CHARMM-GUI Nanodisc Builder for modeling and simulation of various nanodisc systems

Nanodiscs are discoidal protein–lipid complexes that have wide applications in membrane protein studies. Modeling and simulation of nanodiscs are challenging due to the absence of structures of many membrane scaffold proteins (MSPs) that wrap around the membrane bilayer. We have developed CHARMM-GUI Nanodisc Builder ( http://www.charmm-gui.org/input/nanodisc ) to facilitate the setup of nanodisc simulation systems by modeling the MSPs with defined size and known structural features. A total of 11 different nanodiscs with a diameter from 80 to 180 Å are made available in both the all-atom CHARMM and two coarse-grained (PACE and Martini) force fields. The usage of the Nanodisc Builder is demonstrated with various simulation systems. The structures and dynamics of proteins and lipids in these systems were analyzed, showing similar behaviors to those from previous all-atom and coarse-grained nanodisc simulations. We expect the Nanodisc Builder to be a convenient and reliable tool for modeling and simulation of nanodisc systems. © 2019 Wiley Periodicals, Inc.     

人工智能在锂离子电池研发中的应用

锂离子电池已成为解决现代社会储能问题的最佳解决方案之一。然而,电池材料和器件开发都是复杂的多变量问题,传统的依赖研究人员进行实验的试错法在电池性能提升方面遇到了瓶颈。人工智能（AI）具有强大的高速、海量数据处理能力,是上述突破研究瓶颈的最具潜力的技术。其中,机器学习 (ML) 算法在评估多维数据变量和集合之间的组合关联方面的独特优势有望帮助研究人员发现不同因素之间的相互作用规律并阐明材料合成和设备制造的机制。本综述总结了锂离子电池传统研究方法遇到的各种挑战,并详细介绍了人工智能在电池材料研究、电池器件设计与制造、材料与器件表征、电池循环寿命与安全性评估等方面的应用。最重要的是,我们介绍了AI和ML在电池研究中面临的挑战,并讨论了它们应用的缺点和前景。我们相信,未来实验科学家、数学建模专家和AI专家之间更紧密的合作将极大地促进AI和ML方法用以解决传统方法难以克服的电池和材料问题。     

Bridging the Two Worlds: A Universal Interface between Enzymatic and DNA Computing Systems

Molecular computing based on enzymes or nucleic acids has attracted a great deal of attention due to the perspectives of controlling living systems in the way we control electronic computers. Enzyme‐based computational systems can respond to a great variety of small molecule inputs. They have the advantage of signal amplification and highly specific recognition. DNA computing systems are most often controlled by oligonucleotide inputs/outputs and are capable of sophisticated computing as well as controlling gene expressions. Here, we developed an interface that enables communication of otherwise incompatible nucleic‐acid and enzyme‐computational systems. The enzymatic system processes small molecules as inputs and produces NADH as an output. The NADH output triggers electrochemical release of an oligonucleotide, which is accepted by a DNA computational system as an input. This interface is universal because the enzymatic and DNA computing systems are independent of each other in composition and complexity.     

Web-based cheminformatics and molecular property prediction tools supporting drug design and development at Novartis

Web-based tools offer many advantages for processing chemical information, most notably ease of use and high interactivity. Therefore more and more pharmaceutical companies are using web technology to deliver sophisticated molecular processing tools directly to the desks of their chemists, to assist them in the process of designing and developing new drugs. In this paper, the web-based cheminformatics system developed at Novartis and currently used by more than thousand users is described. The system allows various molecular modeling and molecular processing tasks, including the calculation of molecular and substituent properties, property-based virtual screening, visualization of molecules, bioisosteric design, diversity analysis, and support of combinatorial chemistry. The methodology to calculate various molecular properties relevant to drug design is described, including the prediction of intestinal absorption, blood-brain barrier penetration, efflux, and water solubility. Information about the web technology used is also provided.     

Die Welt wird flacher. Beitrag der Chemie zum flachen Fernseher

Shorter switching times, a higher resolution, larger – and ever more widely used: that is a quick summary of the success story of LC displays. The secret behind it: liquid crystalline chemicals which are high‐tech materials with highly advantageous properties which allow for low‐weight and flat LCDs exhibiting a low power consumption. LCDs have made modern communication mobile and visible. For many years Merck has been the market and technology leader in the area of liquid crystals, of their mixtures and applications.     

Epik: a software program for pK<Subscript><Emphasis Type="Italic">a</Emphasis></Subscript> prediction and protonation state generation for drug-like molecules

Epik is a computer program for predicting pK_a values for drug-like molecules. Epik can use this capability in combination with technology for tautomerization to adjust the protonation state of small drug-like molecules to automatically generate one or more of the most probable forms for use in further molecular modeling studies. Many medicinal chemicals can exchange protons with their environment, resulting in various ionization and tautomeric states, collectively known as protonation states. The protonation state of a drug can affect its solubility and membrane permeability. In modeling, the protonation state of a ligand will also affect which conformations are predicted for the molecule, as well as predictions for binding modes and ligand affinities based upon protein–ligand interactions. Despite the importance of the protonation state, many databases of candidate molecules used in drug development do not store reliable information on the most probable protonation states. Epik is sufficiently rapid and accurate to process large databases of drug-like molecules to provide this information. Several new technologies are employed. Extensions to the well-established Hammett and Taft approaches are used for pK_a prediction, namely, mesomer standardization, charge cancellation, and charge spreading to make the predicted results reflect the nature of the molecule itself rather just for the particular Lewis structure used on input. In addition, a new iterative technology for generating, ranking and culling the generated protonation states is employed. Electronic Supplementary Material The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

基于UWP跨平台移动式可燃气体检测报警器智能检定系统的研制

将UWP(Windows通用应用平台)技术应用于可燃气体检测报警器智能检定系统的设计,研制移动式可燃气体检测报警器智能检定系统。该系统包括移动端和PC端,移动端包括委托单信息处理、检定数据处理、数据上传、XML数据管理4个模块;PC端包括上传信息读取、委托单/证书/记录生成及打印两个模块。该系统的研制实现了检定系统在手机、平板、智能检定工作台等的跨平台应用,提高了报警器的检定效率。     

MOCASSIN: Metropolis and kinetic Monte Carlo for solid electrolytes

The combination of density functional theory and Monte Carlo simulations is a powerful approach for the atomistic modeling of defect transport in solid electrolytes. The present contribution introduces the MOCASSIN software (Monte Carlo for Solid State Ionics) for kinetic and Metropolis Monte Carlo simulations of crystalline materials. MOCASSIN combines model building, visualization, and simulation, aiming to provide accessible MC for end users. Developed for the investigation of solid electrolytes, MOCASSIN is ideal for screening common variation parameters, such as temperature and doping fraction. The input effort is minimized using space groups for processing symmetry. The graphical interface for model building allows complex model input, including multiple mobile species, multiple migration paths, small polaron hopping, vehicle movements, multiple complex migration mechanisms, and custom interaction clusters. The software is provided free of charge for noncommercial usage.     

The role of UHPLC in pharmaceutical development

Pharmaceutical separations can be divided into three categories: high throughput, high productivity, and high resolution. These categories contain specific pharmaceutical applications, each of which has distinct separation goals. Traditionally, these goals have been achieved utilizing conventional HPLC with typical column dimensions and particle sizes. The recent introduction of ultra-HPLC (UHPLC) has provided a new potential for method development and analysis. Pharmaceutical chemists must determine the impact of this emerging technology. UHPLC is achieved by using sub-2 microm particle size column packing at increased linear velocities. In order to utilize this technology, mobile phase viscosity must be minimized or the chromatography system must be redesigned to withstand an increased backpressure. Today, there are many commercially available UHPLC systems capable of exceeding conventional pressure limits of 400 bar. The advantage of UHPLC over conventional HPLC is the capability to increase the speed without sacrificing efficiency. In comparison to traditional HPLC, our research showed that UHPLC can decrease run times up to 7 x. In addition, for high resolution applications, UHPLC achieved significant efficiency advantages over traditional HPLC. This paper will evaluate the potential roles for utilizing UHPLC in the pharmaceutical industry.     

New Photosensitive High Temperature Polymers for Electronic Applications

A new photosensitive high temperature polymer stable up to 500%deg;C with high photosensitivity and high resolution has been developed. The trend toward a high degree of integration in solid-state technology requires the use of new high temperature photosensitive insulating materials. Toray's Photoneece system provides such versatile polyimide pattern-generation techniques, containing a unique photosensitive polyimide precursor which can be spun or coated on the substrate. The resultant relief of photosensitive polyimide precursor, after exposure to UV light with a mask, development, and cure processing, is transformed into a cyclized aromatic polyimide. The new system has higher photosensitivity and resolution and eliminates three steps in the conventional pattern-making process for integrated circuits, resulting in a significant cost reduction. The characterization of pattern generation, the conversion to polyimide patterns, and the properties of both Photoneece and the patterns are discussed.     

Class identity assignment for amphetamines using neural networks and GC–FTIR data

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC–FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.     

How gamma radiation processing systems are benefiting from the latest advances in information technology

This paper discusses how gamma irradiation plants are putting the latest advances in computer and information technology to use for better process control, cost savings, and strategic advantages.

Some irradiator operations are gaining significant benefits by integrating computer technology and robotics with real-time information processing, multi-user databases, and communication networks. The paper reports on several irradiation facilities that are making good use of client/server LANs, user-friendly graphics interfaces, supervisory control and data acquisition (SCADA) systems, distributed I/O with real-time sensor devices, trending analysis, real-time product tracking, dynamic product scheduling, and automated dosimetry reading. These plants are lowering costs by fast and reliable reconciliation of dosimetry data, easier validation to GMP requirements, optimizing production flow, and faster release of sterilized products to market.

There is a trend in the manufacturing sector towards total automation using “predictive process control”. Real-time verification of process parameters “on-the-run” allows control parameters to be adjusted appropriately, before the process strays out of limits. Applying this technology to the gamma radiation process, control will be based on monitoring the key parameters such as time, and making adjustments during the process to optimize quality and throughput. Dosimetry results will be used as a quality control measurement rather than as a final monitor for the release of the product. Results are correlated with the irradiation process data to quickly and confidently reconcile variations. Ultimately, a parametric process control system utilizing responsive control, feedback and verification will not only increase productivity and process efficiency, but can also result in operating within tighter dose control set points.     

On the use of correction factors for the mathematical modeling of insulator based dielectrophoretic devices

Mathematical modeling is a fundamental component in the development of new microfluidics techniques and devices. Modeling allows for the rapid testing of new system configurations while saving resources. Microscale electrokinetic (EK) techniques have significantly benefited by the advances in modeling programs and software packages. However, EK phenomena are complex to model, as they dynamically affect system characteristics, including the physical properties of the particles and fluid within the system. Insulator‐based dielectrophoresis (iDEP) is an EK technique that has received important attention during the last two decades. In particular, numerous research groups that study iDEP systems employ a combination of modeling and experimentation for developing new iDEP systems. An important fraction of these research groups has adopted the practice of employing “correction factors” to account for EK phenomena that cannot be accurately predicted in their models due to model complexity and limitations in computing resources. The present review article aims to provide the reader with an overview of the most common approaches in the use of correction factors for the modeling of iDEP systems.     

Class identity assignment for amphetamines using neural networks and GC-FTIR data

An exploratory analysis was performed in order to evaluate the feasibility of building of neural network (NN) systems automating the identification of amphetamines necessary in the investigation of drugs of abuse for epidemiological, clinical and forensic purposes. A first neural network system was built to distinguish between amphetamines and nonamphetamines. A second, more refined system, aimed to the recognition of amphetamines according to their toxicological activity (stimulant amphetamines, hallucinogenic amphetamines, nonamphetamines). Both systems proved that discrimination between amphetamines and nonamphetamines, as well as between stimulants, hallucinogens and nonamphetamines is possible (83.44% and 85.71% correct classification rate, respectively). The spectroscopic interpretation of the 40 most important input variables (GC-FTIR absorption intensities) shows that the modeling power of an input variable seems to be correlated with the stability and not with the intensity of the spectral interaction. Thus, discarding variables only because they correspond to spectral windows with weak absorptions does not seem be not advisable.     

Multiphase flow modeling in centrifugal partition chromatography

The separation efficiency in Centrifugal Partition Chromatography (CPC) depends on selection of a suitable biphasic solvent system (distribution ratio, selectivity factor, sample solubility) and is influenced by hydrodynamics in the chambers. Especially the stationary phase retention, the interfacial area for mass transfer and the flow pattern (backmixing) are important parameters. Their relationship with physical properties, operating parameters and chamber geometry is not completely understood and predictions are hardly possible. Experimental flow visualization is expensive and two-dimensional only. Therefore we simulated the flow pattern using a volume-of-fluid (VOF) method, which was implemented in OpenFOAM^®. For the three-dimensional simulation of a rotating FCPC^®-chamber, gravitational centrifugal and Coriolis forces were added to the conservation equation. For experimental validation the flow pattern of different solvent systems was visualized with an optical measurement system. The amount of mobile phase in a chamber was calculated from gray scale values of videos recorded by an image processing routine in ImageJ^®. To visualize the flow of the stationary phase polyethylene particles were used to perform a qualitative particle image velocimetry (PIV) analysis. We found a good agreement between flow patterns and velocity profiles of experiments and simulations. By using the model we found that increasing the chamber depth leads to higher specific interfacial area. Additionally a circular flow in the stationary phase was identified that lowers the interfacial area because it pushes the jet of mobile phase to the chamber wall. The Coriolis force alone gives the impulse for this behavior. As a result the model is easier to handle than experiments and allows 3D prediction of hydrodynamics in the chamber. Additionally it can be used for optimizing geometry and operating parameters for given physical properties of solvent systems.     

Introducing nonlinear,multivariate ‘Predictor Surfaces’ for quantitative modeling of chemical systems with higher-order,coupled predictor variables

Innovations in chemometrics are required for studies of chemical systems which are governed by nonlinear responses to chemical parameters and/or interdependencies (coupling) among these parameters. Conventional and linear multivariate models have limited use for quantitative and qualitative investigations of such systems because they are based on the assumption that the measured data are simple superpositions of several input parameters. ‘Predictor Surfaces’ were developed for studies of more chemically complex systems such as biological materials in order to ensure accurate quantitative analyses and proper chemical modeling for in-depth studies of such systems. Predictor Surfaces are based on approximating nonlinear multivariate model functions by multivariate Taylor expansions which inherently introduce the required coupled and higher-order predictor variables.