jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Martin F. Luna | Andrea M. Ochsner | Véronique Amstutz | Damian von Blarer | Michael Sokolov | Paolo Arosio | Manfred Zinn
Source: Processes (article, 2021-09-01)
Field: Materials Science — Biomaterials

Modeling of Continuous PHA Production by a Hybrid Approach Based on First Principles and Machine Learning Polyhydroxyalkanoates (PHA) are renewable alternatives to traditional oil-derived polymers. PHA can be produced by different microorganisms in continuous culture under specific media composition, which makes the production process both promising and challenging. In order to achieve large productivities while maintaining high yield and efficiency, the continuous culture needs to be operated in the so-called dual nutrient limitation condition, where both the nitrogen and carbon sources are kept at very low concentrations. Mathematical models can greatly assist both design and operation of the bioprocess, but are challenged by the complexity of the system, in particular by the dual nutrient-limited growth phenomenon, where the cells undergo a metabolic shift that abruptly changes their behavior. Traditional, non-structured mechanistic models based on Monod uptake kinetics can be used to describe the bioreactor operation under specific process conditions. However, in the absence of a model description of the metabolic phenomena inside the cell, the extrapolation to a broader operation domain (e.g., different feeding concentrations and dilution rates) may present mismatches between the predictions and the actual process outcomes. Such detailed models may require almost perfect knowledge of the cell metabolism and omic-level measurements, hampering their development. On the other hand, purely data-driven models that learn correlations from experimental data do not require any prior knowledge of the process and are therefore unbiased and flexible. However, many more data are required for their development and their extrapolation ability is limited to conditions that are similar to the ones used for training. An attractive alternative is the combination of the extrapolation power of first principles knowledge with the flexibility of machine learning methods. This approach results in a hybrid model for the growth and uptake rates that can be used to predict the dynamic operation of the bioreactor. Here we develop a hybrid model to describe the continuous production of PHA by Pseudomonas putida GPo1 culture. After training, the model with experimental data gained under different dilution rates and medium compositions, we demonstrate how the model can describe the process in a wide range of operating conditions, including both single and dual nutrient-limited growth. Biochemical engineering ; Bioprocess ; Process (computing) ; Computer science ; Extrapolation ; Polyhydroxyalkanoates ; Production (economics) ; Process engineering ; Biological system ; Mathematics ; Engineering ; Biology biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; 3D Printing in Biomedical Research
Authors: Angélica Lizeth Sánchez-López | Yocanxóchitl Perfecto‐Avalos | A. Sánchez-Martínez | O. Ceballos-Sánchez | Maricruz Sepúlveda-Villegas | Gabriel Rincón‐Enríquez | Vicente Rodríguez‐González | Rebeca García‐Varela | Luis Marcelo Lozano | Diego E. Navarro-López | Gildardo Sanchez‐Ante | Kaled Corona-Romero | Edgar R. López-Mena
Source: Applied Surface Science (article, 2021-11-01)
Field: Materials Science — Materials Chemistry

Influence of erbium doping on zinc oxide nanoparticles: Structural, optical and antimicrobial activity Absorbance ; Antimicrobial ; Nanoparticle ; Polyvinyl alcohol ; Antibacterial activity ; Materials science ; Crystallite ; X-ray photoelectron spectroscopy ; Escherichia coli ; Nuclear chemistry ; Chemistry ; Analytical Chemistry (journal) ; Chemical engineering ; Nanotechnology ; Chromatography ; Crystallography ; Bacteria ; Organic chemistry ; Composite material ; Biochemistry ; Biology Nanoparticles: synthesis and applications ; ZnO doping and properties ; Quantum Dots Synthesis And Properties
Authors: Zach Free | Maya Hernandez | Mustafa Mashal | Kunal Mondal
Source: Energies (review, 2021-12-17)
Field: Materials Science — Materials Chemistry

A Review on Advanced Manufacturing for Hydrogen Storage Applications Hydrogen is a notoriously difficult substance to store yet has endless energy applications. Thus, the study of long-term hydrogen storage, and high-pressure bulk hydrogen storage have been the subject of much research in the last several years. To create a research path forward, it is important to know what research has already been done, and what is already known about hydrogen storage. In this review, several approaches to hydrogen storage are addressed, including high-pressure storage, cryogenic liquid hydrogen storage, and metal hydride absorption. Challenges and advantages are offered based on reported research findings. Since the project looks closely at advanced manufacturing, techniques for the same are outlined as well. There are seven main categories into which most rapid prototyping styles fall. Each is briefly explained and illustrated as well as some generally accepted advantages and drawbacks to each style. An overview of hydrogen adsorption on metal hydrides, carbon fibers, and carbon nanotubes are presented. The hydrogen storage capacities of these materials are discussed as well as the differing conditions in which the adsorption was performed under. Concepts regarding storage shape and materials accompanied by smaller-scale advanced manufacturing options for hydrogen storage are also presented. Hydrogen storage ; Hydrogen ; Liquid hydrogen ; Energy storage ; Process engineering ; Nanotechnology ; Adsorption ; Materials science ; Computer science ; Engineering ; Chemistry ; Physics ; Organic chemistry Hydrogen Storage and Materials ; Hybrid Renewable Energy Systems ; Advanced Battery Technologies Research
Authors: Marianne Liebi | Viviane Lutz‐Bueno | Manuel Guizar‐Sicairos | Bernd M. Schönbauer | Johannes Eichler | Elisabeth Martinelli | Jörg F. Löffler | Annelie‐Martina Weinberg | Helga C. Lichtenegger | Tilman A. Grünewald
Source: Acta Biomaterialia (article, 2021-07-29)
Field: Materials Science — Biomaterials

3D nanoscale analysis of bone healing around degrading Mg implants evaluated by X-ray scattering tensor tomography The nanostructural adaptation of bone is crucial for its biocompatibility with orthopedic implants. The bone nanostructure also determines its mechanical properties and performance. However, the bone's temporal and spatial nanoadaptation around degrading implants remains largely unknown. Here, we present insights into this important bone adaptation by applying scanning electron microscopy, elemental analysis, and small-angle X-ray scattering tensor tomography (SASTT). We extend the novel SASTT reconstruction method and provide a 3D scattering reciprocal space map per voxel of the sample's volume. From this reconstruction, parameters such as the thickness of the bone mineral particles are quantified, which provide additional information on nanostructural adaptation of bone during healing. We selected a rat femoral bone and a degrading ZX10 magnesium implant as model system, and investigated it over the course of 18 months, using a sham as control. We observe that the bone's nanostructural adaptation starts with an initially fast interfacial bone growth close to the implant, which spreads by a re-orientation of the nanostructure in the bone volume around the implant, and is consolidated in the later degradation stages. These observations reveal the complex bulk bone-implant interactions and enable future research on the related biomechanical bone responses. STATEMENT OF SIGNIFICANCE: Traumatic bone injuries are among the most frequent causes of surgical treatment, and often require the placement of an implant. The ideal implant supports and induces bone formation, while being mechanically and chemically adapted to the bone structure, ensuring a gradual load transfer. While magnesium implants fulfill these requirements, the nanostructural changes during bone healing and implant degradation remain not completely elucidated. Here, we unveil these processes in rat femoral bones with ZX10 magnesium implants and show different stages of bone healing in such a model system. Materials science ; Biomedical engineering ; Implant ; Nanostructure ; X-ray microtomography ; Cortical bone ; Osseointegration ; Nanotechnology ; Medicine ; Radiology ; Anatomy ; Surgery Magnesium Alloys: Properties and Applications ; Bone Tissue Engineering Materials ; Microstructure and mechanical properties
Authors: Zhao Li | Benjamin J. Bucior | Haoyuan Chen | Maciej Harańczyk | J. Ilja Siepmann | Randall Q. Snurr
Source: The Journal of Chemical Physics (article, 2021-07-01)
Field: Chemistry — Inorganic Chemistry

Machine learning using host/guest energy histograms to predict adsorption in metal–organic frameworks: Application to short alkanes and Xe/Kr mixtures A machine learning (ML) methodology that uses a histogram of interaction energies has been applied to predict gas adsorption in metal-organic frameworks (MOFs) using results from atomistic grand canonical Monte Carlo (GCMC) simulations as training and test data. In this work, the method is first extended to binary mixtures of spherical species, in particular, Xe and Kr. In addition, it is shown that single-component adsorption of ethane and propane can be predicted in good agreement with GCMC simulation using a histogram of the adsorption energies felt by a methyl probe in conjunction with the random forest ML method. The results for propane can be improved by including a small number of MOF textural properties as descriptors. We also discuss the most significant features, which provides physical insight into the most beneficial adsorption energy sites for a given application. Adsorption ; Propane ; Metal-organic framework ; Histogram ; Work (physics) ; Binary number ; Monte Carlo method ; Energy (signal processing) ; Xenon ; Materials science ; Krypton ; Chemistry ; Thermodynamics ; Physical chemistry ; Computer science ; Physics ; Organic chemistry ; Artificial intelligence ; Image (mathematics) ; Mathematics Metal-Organic Frameworks: Synthesis and Applications ; Machine Learning in Materials Science ; X-ray Diffraction in Crystallography
Authors: Zeming He | Ming Yang | Lei Wang | Ergude Bao | Hang Zhang
Source: Engineered Science (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Concentrated Photovoltaic Thermoelectric Hybrid System: An Experimental and Machine Learning Study Applying solar energy over a wider spectral range can lead to more efficient energy conversion. The combination of a photovoltaic (PV) cell and a thermoelectric generator (TEG) is a widely studied technology for effectively broadening the use of the solar spectrum. In this paper, we select two kinds of photovoltaic cells and combine them with a TEG to form different systems, and analyze the overall performance of each system to provide a certain reference for optimal use of photovoltaic cells and a TEG in a hybrid system. Furthermore, we use machine learning to optimize the structural parameters of the hybrid system, and predict the optimal output power of the system when the area ratio of the TEG and PV module is 4.41. This work provides an important reference for further research on the PV-TEG hybrid system and its applications. Photovoltaic system ; Thermoelectric generator ; Hybrid system ; Computer science ; Solar energy ; Generator (circuit theory) ; Automotive engineering ; Thermoelectric effect ; Power (physics) ; Electrical engineering ; Engineering ; Machine learning ; Physics Advanced Thermoelectric Materials and Devices ; Thermal Radiation and Cooling Technologies ; Transition Metal Oxide Nanomaterials
Authors: M. Greaves | Mana Mende | Jiacheng Wang | Wenji Yang | Suelen Barg
Source: Journal of materials research/Pratt's guide to venture capital sources (article, 2021-07-08)
Field: Materials Science — Materials Chemistry

Investigating the rheology of 2D titanium carbide (MXene) dispersions for colloidal processing: Progress and challenges Abstract Among 2D materials, MXenes (especially their most studied member, titanium carbide) present a unique opportunity for application via colloidal processing, as they are electrically conductive and chemically active, whilst still being easily dispersed in water. And since the first systematic study of colloidal MXene rheology was published in 2018 ( Rheological Characteristics of 2D Titanium Carbide (MXene) Dispersions: A Guide for Processing MXenes by Akuzum, et al.), numerous works have presented small amounts of rheological data which together contribute to a deeper understanding of the topic. This work reviews the published rheological data on all MXene-containing formulations, including liquid crystals, mixtures and non-aqueous colloids, which have been used in processes such as stamping, patterning, 2D and 3D printing. An empirical model of aqueous titanium carbide viscosity has been developed, and recommendations are made to help researchers more effectively present their data for future rheological analysis. Graphic abstract MXenes ; Rheology ; Titanium carbide ; Materials science ; Carbide ; Colloid ; Nanotechnology ; Titanium ; Chemical engineering ; Metallurgy ; Composite material ; Engineering MXene and MAX Phase Materials ; Advanced Sensor and Energy Harvesting Materials ; Dielectric materials and actuators
Authors: Ángela Valentín‐Pérez | Patrick Rosa | Elizabeth A. Hillard | Michel Giorgi
Source: Chirality (review, 2021-11-11)
Field: Chemistry — Spectroscopy

Chirality determination in crystals This tutorial review article discusses chirality determination in the solid state, both in single crystals and in crystal assemblies, with an emphasis on X-ray diffraction. The main principles of using X-ray diffraction to reliably determine absolute structure are summarized, and the complexity which can be encountered in chiral structures-kryptoracemates, scalemates, and inversion twinning-is illustrated with examples from our laboratories and the literature. We then address the problem of the bulk crystallization and discuss different techniques to determine chirality in a large assembly of crystal structures, with a special prominence given to an X-ray natural circular dichroism mapping technique that we recently reported. Chirality (physics) ; Chemistry ; Circular dichroism ; Crystallography ; Diffraction ; Crystallization ; Crystal structure ; Inversion (geology) ; Optics ; Physics ; Organic chemistry ; Quantum mechanics ; Chiral symmetry Molecular spectroscopy and chirality ; Crystallography and molecular interactions ; Crystallization and Solubility Studies
Authors: Omry Cohen | Or Malka | Zohar Ringel
Source: Physical Review Research (article, 2021-04-09)
Field: Materials Science — Materials Chemistry

Learning curves for overparametrized deep neural networks: A field theory perspective In the past decade, deep neural networks (DNNs) came to the fore as the leading machine-learning algorithms for a variety of tasks. Their rise was founded on market needs and engineering craftsmanship, the latter based more on trial and error than on theory. While still far behind the application forefront, the theoretical study of DNNs has recently made important advancements in analyzing the highly overparametrized regime where some exact results have been obtained. Leveraging these ideas and adopting a more physicslike approach, here we construct a versatile field theory formalism for supervised deep learning, involving renormalization group, Feynman diagrams, and replicas. In particular, we show that our approach leads to highly accurate predictions of learning curves of truly deep DNNs trained on polynomial regression problems. It also explains in a concrete manner why DNNs generalize well despite being highly overparametrized, this due to an entropic bias to simple functions which, for the case of fully connected DNNs with data sampled on the hypersphere, are low-order polynomials in the input vector. Being a complex interacting system of artificial neurons, we believe that such tools and methodologies borrowed from condensed matter physics would prove essential for obtaining an accurate quantitative understanding of deep learning. Deep neural networks ; Deep learning ; Formalism (music) ; Artificial neural network ; Renormalization ; Variety (cybernetics) ; Field (mathematics) ; Construct (python library) Machine Learning in Materials Science ; Quantum many-body systems ; Model Reduction and Neural Networks
Authors: Jean Frederic Isingizwe Nturambirwe | W.J. Perold | Umezuruike Linus Opara
Source: Sensors (article, 2021-07-22)
Field: Chemistry — Analytical Chemistry

Classification Learning of Latent Bruise Damage to Apples Using Shortwave Infrared Hyperspectral Imaging Bruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks. However, ESD models had the highest classification accuracy in quantitative (>85%) models and were found to be relatively better suited for such a multiple category classification problem than the rest. Bruise ; Artificial intelligence ; Linear discriminant analysis ; Hyperspectral imaging ; Pattern recognition (psychology) ; Support vector machine ; Computer science ; Subspace topology ; Dimensionality reduction ; YCbCr ; Computer vision ; Machine learning ; Image processing ; Medicine ; Image (mathematics) Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Plant Pathogens and Fungal Diseases
Authors: Keke Qi | Liutian Wu | Chengyuan Liu | Yang Pan
Source: Metabolites (review, 2021-11-15)
Field: Chemistry — Spectroscopy

Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis Ambient mass spectrometry imaging (AMSI) has attracted much attention in recent years. As a kind of unlabeled molecular imaging technique, AMSI can enable in situ visualization of a large number of compounds in biological tissue sections in ambient conditions. In this review, the developments of various AMSI techniques are discussed according to one-step and two-step ionization strategies. In addition, recent applications of AMSI for lipid and metabolite analysis (from 2016 to 2021) in disease diagnosis, animal model research, plant science, drug metabolism and toxicology research, etc., are summarized. Finally, further perspectives of AMSI in spatial resolution, sensitivity, quantitative ability, convenience and software development are proposed. Mass spectrometry imaging ; Mass spectrometry ; Metabolite ; Metabolomics ; Computer science ; Chemistry ; Environmental chemistry ; Chromatography ; Biochemistry Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Puneet Mishra | Dário Passos
Source: Journal of Chemometrics (article, 2021-07-21)
Field: Chemistry — Analytical Chemistry

Deep chemometrics: Validation and transfer of a global deep near‐infrared fruit model to use it on a new portable instrument Abstract Recently, a large near‐infrared spectroscopy data set for mango fruit quality assessment was made available online. Based on that data, a deep learning (DL) model outperformed all major chemometrics and machine learning approaches. However, in earlier studies, the model validation was limited to the test set from the same data set which was measured with the same instrument on samples from a similar origin. From a DL perspective, once a model is trained it is expected to generalise well when applied to a new batch of data. Hence, this study aims to validate the generalisability performance of the earlier developed DL model related to DM prediction in mango on a different test set measured in a local laboratory setting, with a different instrument. At first, the performance of the old DL model was presented. Later, a new DL model was crafted to cover the seasonal variability related to fruit harvest season. Finally, a DL model transfer method was performed to use the model on a new instrument. The direct application of the old DL model led to a higher error compared to the PLS model. However, the performance of the DL model was improved drastically when it was tuned to cover the seasonal variability. The updated DL model performed the best compared to the implementation of a new PLS model or updating the existing PLS model. A final root‐mean‐square error prediction (RMSEP) of 0.518% was reached. This result supports that, in the availability of large data sets, DL modelling can outperform chemometrics approaches. Chemometrics ; Test set ; Data set ; Set (abstract data type) ; Training set ; Mean squared error ; Near-infrared spectroscopy ; Computer science ; Partial least squares regression ; Artificial intelligence ; Mathematics ; Machine learning ; Statistics ; Pattern recognition (psychology) ; Data mining Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies