Machine learning in physico-chemical processes metamodeling

The method of conjugate deviations based on the regression analysis has been suggested for construction of a new nucleophilicity scale. This method has been applied to a set of 28 nucleophiles participating in 47 physical and chemical processes described in literature. The two-parameter nucleophilicity scale obtained represents-in the parameter denoted as ND-the general tendency to form a bond to an electrophile predominantly on the basis of the orbital interaction and-in the parameter denoted as PD-the ability to interact with a centre similar to the proton (basicity). The linear correlation equation involving the ND, PD parameters and the charge appears to be distinctly better than the most significant relations used. The correlation dependences have the physico-chemical meaning. From the position of individual nucleophiles in the space of the ND and PD parameters, some general conclusions have been derived about the factors governing the reactivity of nucleophiles.

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Unsupervised Machine Learning and Data Mining Procedures Reveal Short Term, Climate Driven Patterns Linking Physico-Chemical Features and Zooplankton Diversity in Small Ponds

Water ◽

10.3390/w13091217 ◽

2021 ◽

Vol 13 (9) ◽

pp. 1217

Author(s):

Nicolò Bellin ◽

Erica Racchetti ◽

Catia Maurone ◽

Marco Bartoli ◽

Valeria Rossi

Keyword(s):

Machine Learning ◽

Fuzzy Sets ◽

Learning Algorithm ◽

Machine Learning Algorithms ◽

Short Term ◽

Unsupervised Machine Learning ◽

Fuzzy C Means ◽

Air Temperatures ◽

Physico Chemical ◽

Shallow Ponds

Machine Learning (ML) is an increasingly accessible discipline in computer science that develops dynamic algorithms capable of data-driven decisions and whose use in ecology is growing. Fuzzy sets are suitable descriptors of ecological communities as compared to other standard algorithms and allow the description of decisions that include elements of uncertainty and vagueness. However, fuzzy sets are scarcely applied in ecology. In this work, an unsupervised machine learning algorithm, fuzzy c-means and association rules mining were applied to assess the factors influencing the assemblage composition and distribution patterns of 12 zooplankton taxa in 24 shallow ponds in northern Italy. The fuzzy c-means algorithm was implemented to classify the ponds in terms of taxa they support, and to identify the influence of chemical and physical environmental features on the assemblage patterns. Data retrieved during 2014 and 2015 were compared, taking into account that 2014 late spring and summer air temperatures were much lower than historical records, whereas 2015 mean monthly air temperatures were much warmer than historical averages. In both years, fuzzy c-means show a strong clustering of ponds in two groups, contrasting sites characterized by different physico-chemical and biological features. Climatic anomalies, affecting the temperature regime, together with the main water supply to shallow ponds (e.g., surface runoff vs. groundwater) represent disturbance factors producing large interannual differences in the chemistry, biology and short-term dynamic of small aquatic ecosystems. Unsupervised machine learning algorithms and fuzzy sets may help in catching such apparently erratic differences.

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Enzymes at solid surfaces: Nature of the interfaces and physico-chemical processes

Electrochimica Acta ◽

10.1016/j.electacta.2008.02.100 ◽

2008 ◽

Vol 54 (1) ◽

pp. 116-122 ◽

Cited By ~ 23

Author(s):

Samuel Caillou ◽

Patrick A. Gerin ◽

Cristèle J. Nonckreman ◽

Sandrine Fleith ◽

Christine C. Dupont-Gillain ◽

...

Keyword(s):

Chemical Processes ◽

Solid Surfaces ◽

Physico Chemical

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Cleaning with Laser Radiation on Istria Stone

MRS Proceedings ◽

10.1557/proc-267-955 ◽

1992 ◽

Vol 267 ◽

Cited By ~ 4

Author(s):

Pagona Maravelaki ◽

G. Biscontin ◽

E. Zendri ◽

R. Polloni ◽

W. Cecchetti

Keyword(s):

Laser Radiation ◽

Laser Treatment ◽

Chemical Processes ◽

Original Material ◽

Stone Surface ◽

Stone Conservation ◽

Physico Chemical

ABSTRACTCleaning treatments necessary for stone conservation consist of removing compounds that were superimposed on the original material by means of physico-chemical processes.,The purpose of this work is to identify the transformation of stone surface by the LASER treatment. Cleaning test on Istria stone with LASER radiation at different energies and in different regimes have been performed. The depletion of the original material has been evaluated by means of SEM microprobe analyses.

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A Machine Learning-based Methodology for Multi-Parametric Solution of Chemical Processes Operation Optimization under Uncertainty

Chemical Engineering Journal ◽

10.1016/j.cej.2021.131632 ◽

2021 ◽

pp. 131632

Author(s):

Ahmed Shokry ◽

Sergio Medina-González ◽

Piero Baraldi ◽

Enrico Zio ◽

Eric Moulines ◽

...

Keyword(s):

Machine Learning ◽

Chemical Processes ◽

Optimization Under Uncertainty ◽

Parametric Solution ◽

Operation Optimization

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X-ray and UV Radiation Damage of dsDNA/Protein Complexes

Molecules ◽

10.3390/molecules26113132 ◽

2021 ◽

Vol 26 (11) ◽

pp. 3132

Author(s):

Paweł Wityk ◽

Dorota Kostrzewa-Nowak ◽

Beata Krawczyk ◽

Michał Michalik ◽

Robert Nowak

Keyword(s):

Dna Damage ◽

Simple Model ◽

Uv Radiation ◽

Protein Complexes ◽

Chemical Processes ◽

Model Systems ◽

Degradation Pathways ◽

X Ray ◽

Physico Chemical ◽

Sensitization Process

Radiation and photodynamic therapies are used for cancer treatment by targeting DNA. However, efficiency is limited due to physico-chemical processes and the insensitivity of native nucleobases to damage. Thus, incorporation of radio- and photosensitizers into these therapies should increase both efficacy and the yield of DNA damage. To date, studies of sensitization processes have been performed on simple model systems, e.g., buffered solutions of dsDNA or sensitizers alone. To fully understand the sensitization processes and to be able to develop new efficient sensitizers in the future, well established model systems are necessary. In the cell environment, DNA tightly interacts with proteins and incorporating this interaction is necessary to fully understand the DNA sensitization process. In this work, we used dsDNA/protein complexes labeled with photo- and radiosensitizers and investigated degradation pathways using LC-MS and HPLC after X-ray or UV radiation.

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Computational Exploration of Functional Nanoscale Carbonaceous Materials

Current Nanoscience ◽

10.2174/1573413717666210924163449 ◽

2021 ◽

Vol 17 ◽

Author(s):

Grigoriy Sereda ◽

Md Tusar Uddin ◽

Jacob Wente

Keyword(s):

Biomedical Applications ◽

Chemical Processes ◽

Computational Power ◽

Carbonaceous Materials ◽

New Materials ◽

New Era ◽

Physico Chemical ◽

Computational Exploration ◽

Properties Of Materials ◽

Computational Research

Background: The unique ability of carbon to form a wide variety of allotrope modifications has ushered a new era in the material science. Tuning the properties of these materials by functionalization is a must-have tool for their design customized for a specific practical use. The exponentially growing computational power available to researchers allows for the prediction and thorough understanding of the underlying physico-chemical processes responsible for the practical properties of pristine and modified carbons using the methods of quantum chemistry. Method: This review focuses on the computational assessment of the influence of functionalization on the properties of carbons and enabling desired practical properties of the new materials. The first section of each part of this review focuses on graphene - nearly planar units built from sp2-carbons. The second section discusses patterns of sp2-carbons rolled-up into curved 3D-structures in a variety of ways (fullerenes). The overview of other types of carbonaceous materials including those with a high abundance of sp3-carbons, including nanodiamonds, can be found in the third section of each manuscript’s part. Conclusion: The computational methods are especially critical for predicting electronic properties of materials such as the band gap, conductivity, optical and photoelectronic properties, solubility, adsorptivity, potential for catalysis, sensing, imaging and biomedical applications. We expect that introduction of defects to carbonaceous materials as a type of their functionalization will be a point of growth in this area of computational research.

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