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![Figure][1] The McPherson 234/302 compact vacuum-ultraviolet spectrometer has a digital grating drive for precise wavelength selection and positioning from 30 nm to 1,100 nm. Micrometer adjustable slits vary from 0.01 mm ~3 mm in width and 2 mm ~20 mm in height. Software is available along with LabVIEW drivers. This instrument's normal incidence design has optional multiple input or output ports. It can also be easily used as a spectrograph with a microchannel plate intensifier or charge-coupled device detector, or as a scanning monochromator—one instrument can do both functions while remaining under vacuum. There are many options for customization: We can provide ultrahigh vacuum nonmagnetic versions or customized adapters for the customer's vacuum pumps, detectors, or light sources. Double monochromators for exceptionally low stray light and high spectral purity are also available. Special or standard, every instrument ships with certified spectral calibration. Compatible with all 2D-barcoded tube racks in SBS format and with a footprint of only slightly more than one plate/rack position, the Ziath DataPaq Express offers a space-saving way to integrate a fast full-rack scanner onto a liquid-handling robot. Designed with a separate power and processing box that can be positioned under your liquid-handling robot and under your deck, this compact scanner frees up vital deck space. Its uniquely low form factor allows liquid-handling robots easy gripper access to simply pick up and dispense from racks on top of the scanner. Offering rapid image scanning and decoding in just 2 s, the camera-based DataPaq Express will also help improve your robotic workflow. Baseplates and drivers are available to enable easy integration with most commercial liquid-handling robots. The Smart Evaporator C1 from BioChromato is an easy-to-use, affordable system optimized to concentrate or dry single samples directly from any tube or vial (up to 32-mm neck diameter) in even high-boiling solvents such as DMSO, DMF, or water. Drawing on BioChromato's patented spiral plug evaporation technology, the compact, benchtop system offers fast, effective evaporation in tubes or vials without solvent bumping, eliminating the risk of sample loss and cross-contamination and saving valuable time. The Smart Evaporator C1 can handle solvent volumes up to 40 mL, which can be extremely useful for concentrating compounds after organic synthesis or for drying analytical samples at relatively high speeds. The versatile C1 can also manage small tubes and vials (e.g., 1.5 mL) where solvent volumes can be as little as 0.1 mL or less. Porvair Sciences provides a complete design and manufacture service to help customers develop new and innovative custom microplates for specialist applications. We are widely recognized as a leader in the field of molding ultrapure plastic materials such as polystyrene, polypropylene, and polycarbonate. Decades of experience in ultrasonic welding, surface treatment techniques, co-sintering of polymers/silicas, and specialist assembly, combined with a strong understanding of analytical applications, make us an ideal OEM partner for development and production of optimized custom microplate solutions. From single-well to 1,536-well microplates, Porvair Sciences has the knowledge, expertise, and flexibility to design and manufacture to customer specifications. Our team of engineers and creative thinkers allows us to develop high-quality products for filtration, storage, and separation and to push the boundaries of microplate design for the life science and analytical markets. With its unique safety-locking mechanism and robust, adjustable support frame/lifting platform option, the Multicell PLUS high-pressure reactor from Asynt sets a new benchmark for operator safety, all-round accessibility, and ease-of-use. Manufactured from 316 stainless steel, the unit operates at pressures up to 50 barg and temperatures up to 200ºC. Asynt offers options for the system to be manufactured from alternative materials that can withstand highly corrosive/caustic chemicals, and for increased operational conditions up to 200 barg and temperatures of over 300ºC. While the Multicell PLUS accommodates 8 × 30 mL cells as standard, options are offered for 4-, 6-, and 10-cell arrangements with individual cell volumes up to 100 mL. Motor-driven, magnetically coupled overhead stirring is also offered as an option for more viscous reaction mixtures. Optional independent isolation of each cell allows the user to charge each vessel with differing chemistry and pressures without cross-contamination between cells. Milo is the world's first automated single-cell Western (scWestern) platform. The instrument measures protein expression in thousands of cells in a single run, allowing you to profile heterogeneity in your samples through single-cell analysis. Just load your cell suspension, and the scWest chip captures ~1,000 single cells. Milo then performs a fast, 1-min SDS-PAGE (sodium dodecyl sulphate–polyacrylamide gel electrophoresis) separation on each single-cell lysate on-chip. Then just probe with your favorite conventional Western blot antibodies to measure ~12 proteins per cell using a variety of multiplexing strategies. Milo's Single-Cell Western technology unlocks the single-cell proteome to measure more of the proteome than is possible with any other single-cell protein analysis technique. [1]: pending:yes


New Products

Science

![Figure][1] Eppendorf now offers a further building block in supporting scientists with tailored solutions for the daily lab routine. Controlled, reliable cell thawing is mandatory for further downstream experiments in every cell-handling lab. Although water-bath based or even manual thawing of cells is still commonly practiced, these methods are not as desirable due to their limited reproducibility. The Eppendorf ThermoMixer C now features an exchangeable thermoblock, the cryo-thaw SmartBlock, which provides a dedicated thawing program for reproducible, reliable thawing of cells from frozen storage conditions up to 37°C. In response to the community's need for highly specific and reproducible antibodies for SARS-CoV-1/2 research, MilliporeSigma has designed ZooMAb recombinant monoclonal antibodies against various SARS CoV 1/2 targets. ZooMAb antibodies are all recombinantly produced, lyophilized, and free of animal components. Explore our offering of ZooMAb recombinant monoclonal antibodies that are suitable for COVID-19 research: Anti-SARS-CoV-1/2 NP, clone 1C7C7 ZooMAb Mouse Monoclonal Nucleoprotein; Anti-SARS-CoV-1/2 S Protein, clone 2B3E5 ZooMAb Mouse Monoclonal SARS-CoV-1/2 Spike Glycoprotein; and Anti-SARS-CoV-1/2 S Protein clone hu2B3E5 ZooMAb Chimeric Monoclonal. The BioScaffolder Prime from Analytik is an affordable, high-performance 3D bioprinter that delivers precision engineering in an advanced, customizable platform. The rapidly expanding field of 3D bioprinting for tissue engineering and regenerative medicine combines biocompatible/biodegradable polymers with living cells. This bioprinter package offers researchers the ability to create bioscaffolds for cell growth and to deposit layers of bioinks on implants or microfluidic objects. The unit can be equipped with multiple dispensing tools, including unique core/shell tools for simultaneous dispensing of different materials. Decentralized units for printing, media control, and computing save precious space in your biosafety cabinet and ensure superb heat dissipation. Silent but smart XYZ-drives deliver micrometer precision. In addition, the system comes with a Peltier heater/cooler cartridge for temperature-controlled bioprinting and a built-in UV-source UV-LED pen. Designed to fit and operate in a standard biosafety cabinet, BioScaffolder Prime enables you to undertake your 3D printing applications quickly, safely, and in a sterile environment. Ziath reports strong uptake of its Mohawk semiautomated tube picker in smaller biobanks and biorepositories, which need to select tubes from cold racks straight from the freezer but cannot afford the huge investment in robotics required to automatically pick and place tubes. The small, compact Mohawk can pick up 16 tubes simultaneously from a 96-position tube rack. By elevating sample tubes in racks using solenoids, the Mohawk enables biobank operators to quickly retrieve the correct tubes and put them in the destination racks. Additionally, because the Mohawk can seamlessly connect with Ziath rack scanners, biobank users can read a picking list, select tubes, and verify that the correct tubes are picked—making the process of finding and selecting the right tubes in your biobank more efficient and economical. The Cold Coil II Flow Reactor Module from Uniqsis is a flexible, entry-level solution for low temperature flow chemistry applications. Used in conjunction with an external thermoregulation circulator, the unit can maintain stable temperatures between −78°C and 150°C for extended periods of time. It is compatible with all Uniqsis coil reactors, from 2.0 mL up to 60 mL capacity. A proprietary clamping mechanism holds the coil reactor firmly in place and ensures optimal thermal contact while allowing easy interchange of coil reactors. The Cold Coil II can be easily converted into a photoreactor by coupling it with a Uniqsis PhotoSyn high-power LED light module. It is also compatible with the Uniqsis HotColumn multiple-column reactor adaptor for packed-bed applications. To ensure accurate remote measurement of the Cold Coil II reactor temperature, an optional internal temperature probe can be connected directly via RS232C. The RAPID EPS (Easy Piercing Seal) from BioChromato is designed for scientists looking to prevent contamination issues and autosampler-needle clogging when accessing samples stored in 96-well microplates ready for LC/MS analysis. For LC/MS users, a key criterion for an effective microplate seal is its resistance to solvents such as acetonitrile, methanol, and DMSO, which are commonly used in experiments and analysis. The RAPID EPS uses a synthetic rubber adhesive to create a high-integrity, airtight seal with microplates, and shows no contamination in the eluents. In addition, the unique construction of BioChromato's RAPID EPS does not leave particulate material when pierced, further safeguarding your samples from contamination and eliminating potentially harmful effects to your LC/MS autosampler. The RAPID EPS is proven to offer dependable microplate sealing over a working temperature range of −80°C to 80°C.  [1]: pending:yes


Machine learning of solvent effects on molecular spectra and reactions

#artificialintelligence

Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance.


Machine learning of solvent effects on molecular spectra and reactions

arXiv.org Machine Learning

Fast and accurate simulation of complex chemical systems in environments such as solutions is a long standing challenge in theoretical chemistry. In recent years, machine learning has extended the boundaries of quantum chemistry by providing highly accurate and efficient surrogate models of electronic structure theory, which previously have been out of reach for conventional approaches. Those models have long been restricted to closed molecular systems without accounting for environmental influences, such as external electric and magnetic fields or solvent effects. Here, we introduce the deep neural network FieldSchNet for modeling the interaction of molecules with arbitrary external fields. FieldSchNet offers access to a wealth of molecular response properties, enabling it to simulate a wide range of molecular spectra, such as infrared, Raman and nuclear magnetic resonance. Beyond that, it is able to describe implicit and explicit molecular environments, operating as a polarizable continuum model for solvation or in a quantum mechanics / molecular mechanics setup. We employ FieldSchNet to study the influence of solvent effects on molecular spectra and a Claisen rearrangement reaction. Based on these results, we use FieldSchNet to design an external environment capable of lowering the activation barrier of the rearrangement reaction significantly, demonstrating promising venues for inverse chemical design.


Scientific intuition inspired by machine learning generated hypotheses

arXiv.org Artificial Intelligence

Machine learning with application to questions in the physical sciences has become a widely used tool, successfully applied to classification, regression and optimization tasks in many areas. Research focus mostly lies in improving the accuracy of the machine learning models in numerical predictions, while scientific understanding is still almost exclusively generated by human researchers analysing numerical results and drawing conclusions. In this work, we shift the focus on the insights and the knowledge obtained by the machine learning models themselves. In particular, we study how it can be extracted and used to inspire human scientists to increase their intuitions and understanding of natural systems. We apply gradient boosting in decision trees to extract human interpretable insights from big data sets from chemistry and physics. In chemistry, we not only rediscover widely know rules of thumb but also find new interesting motifs that tell us how to control solubility and energy levels of organic molecules. At the same time, in quantum physics, we gain new understanding on experiments for quantum entanglement. The ability to go beyond numerics and to enter the realm of scientific insight and hypothesis generation opens the door to use machine learning to accelerate the discovery of conceptual understanding in some of the most challenging domains of science.


Machine-learning Prediction Of Infrared Spectra Of Interstellar Polycyclic Aromatic Hydrocarbons - Astrobiology

#artificialintelligence

We design and train a neural network (NN) model to efficiently predict the infrared spectra of interstellar polycyclic aromatic hydrocarbons (PAHs) with a computational cost many orders of magnitude lower than what a first-principles calculation would demand. The input to the NN is based on the Morgan fingerprints extracted from the skeletal formulas of the molecules and does not require precise geometrical information such as interatomic distances. The model shows excellent predictive skill for out-of-sample inputs, making it suitable for improving the mixture models currently used for understanding the chemical composition and evolution of the interstellar medium. We also identify the constraints to its applicability caused by the limited diversity of the training data and estimate the prediction errors using a ensemble of NNs trained on subsets of the data. The power of these topological descriptors is demonstrated by the limited effect of including detailed geometrical information in the form of Coulomb matrix eigenvalues.


Machine Learning Force Fields

arXiv.org Machine Learning

In recent years, the use of Machine Learning (ML) in computational chemistry has enabled numerous advances previously out of reach due to the computational complexity of traditional electronic-structure methods. One of the most promising applications is the construction of ML-based force fields (FFs), with the aim to narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs. The key idea is to learn the statistical relation between chemical structure and potential energy without relying on a preconceived notion of fixed chemical bonds or knowledge about the relevant interactions. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. The core concepts underlying ML-FFs are described in detail and a step-by-step guide for constructing and testing them from scratch is given. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs.


One-Shot learning based classification for segregation of plastic waste

arXiv.org Artificial Intelligence

The problem of segregating recyclable waste is fairly daunting for many countries. This article presents an approach for image based classification of plastic waste using one-shot learning techniques. The proposed approach exploits discriminative features generated via the siamese and triplet loss convolutional neural networks to help differentiate between 5 types of plastic waste based on their resin codes. The approach achieves an accuracy of 99.74% on the WaDaBa Database


Spectral Flow on the Manifold of SPD Matrices for Multimodal Data Processing

arXiv.org Machine Learning

In this paper, we consider data acquired by multimodal sensors capturing complementary aspects and features of a measured phenomenon. We focus on a scenario in which the measurements share mutual sources of variability but might also be contaminated by other measurement-specific sources such as interferences or noise. Our approach combines manifold learning, which is a class of nonlinear data-driven dimension reduction methods, with the well-known Riemannian geometry of symmetric and positive-definite (SPD) matrices. Manifold learning typically includes the spectral analysis of a kernel built from the measurements. Here, we take a different approach, utilizing the Riemannian geometry of the kernels. In particular, we study the way the spectrum of the kernels changes along geodesic paths on the manifold of SPD matrices. We show that this change enables us, in a purely unsupervised manner, to derive a compact, yet informative, description of the relations between the measurements, in terms of their underlying components. Based on this result, we present new algorithms for extracting the common latent components and for identifying common and measurement-specific components.


Principle Component Analysis for Classification of the Quality of Aromatic Rice

arXiv.org Artificial Intelligence

This research introduces an instrument for performing quality control on aromatic rice by utilizing feature extraction of Principle Component Analysis (PCA) method. Our proposed system (DNose v0.2) uses the principle of electronic nose or enose. Enose is a detector instrument that work based on classification of the smell, like function of human nose. It has to be trained first for recognizing the smell before work in classification process. The aim of this research is to build an enose system for quality control instrument, especially on aromatic rice. The advantage of this system is easy to operate and not damaging the object of research. In this experiment, ATMega 328 and 6 gas sensors are involved in the electronic module and PCA method is used for classification process.