Towards Understanding Aerogels' Efficiency for Oil Removal-A Principal Component Analysis Approach.

In this study, our aim was to estimate the adsorption potential of three families of aerogels: nanocellulose (NC), chitosan (CS), and graphene (G) oxide-based aerogels. The emphasized efficiency to seek here concerns oil and organic contaminant removal. In order to achieve this goal, principal component analysis (PCA) was used as a data mining tool. PCA showed hidden patterns that were not possible to seek by the bi-dimensional conventional perspective. In fact, higher total variance was scored in this study compared with previous findings (an increase of nearly 15%). Different approaches and data pre-treatments have provided different findings for PCA. When the whole dataset was taken into consideration, PCA was able to reveal the discrepancy between nanocellulose-based aerogel from one part and chitosan-based and graphene-based aerogels from another part. In order to overcome the bias yielded by the outliers and to probably increase the degree of representativeness, a separation of individuals was adopted. This approach allowed an increase in the total variance of the PCA approach from 64.02% (for the whole dataset) to 69.42% (outliers excluded dataset) and 79.82% (outliers only dataset). This reveals the effectiveness of the followed approach and the high bias yielded from the outliers.

Application of Unsupervised Machine Learning for the Evaluation of Aerogels' Efficiency towards Ion Removal-A Principal Component Analysis (PCA) Approach.

Water scarcity is a global problem affecting millions of people. It can lead to severe economic, social, and environmental consequences. It can also have several impacts on agriculture, industry, and households, leading to a decrease in human quality of life. To address water scarcity, governments, communities, and individuals must work in synergy for the sake of water resources conservation and the implementation of sustainable water management practices. Following this urge, the enhancement of water treatment processes and the development of novel ones is a must. Here, we have investigated the potential of the applicability of "Green Aerogels" in water treatment's ion removal section. Three families of aerogels originating from nanocellulose (NC), chitosan (CS), and graphene (G) are investigated. In order to reveal the difference between aerogel samples in-hand, a "Principal Component Analysis" (PCA) has been performed on the physical/chemical properties of aerogels, from one side, and the adsorption features, from another side. Several approaches and data pre-treatments have been considered to overcome any bias of the statistical method. Following the different followed approaches, the aerogel samples were located in the center of the biplot and were surrounded by different physical/chemical and adsorption properties. This would probably indicate a similar efficiency in the ion removal of the aerogels in-hand, whether they were nanocellulose-based, chitosan-based, or even graphene-based. In brief, PCA has shown a similar efficiency of all the investigated aerogels towards ion removal. The advantage of this method is its capacity to engage and seek similarities/dissimilarities between multiple factors, with the elimination of the shortcomings for the tedious and time-consuming bidimensional data visualization.

Application of Unsupervised Learning for the Evaluation of Aerogels' Efficiency towards Dye Removal-A Principal Component Analysis (PCA) Approach.

Water scarcity is a growing global issue, particularly in areas with limited freshwater sources, urging for sustainable water management practices to insure equitable access for all people. One way to address this problem is to implement advanced methods for treating existing contaminated water to offer more clean water. Adsorption through membranes technology is an important water treatment technique, and nanocellulose (NC)-, chitosan (CS)-, and graphene (G)- based aerogels are considered good adsorbents. To estimate the efficiency of dye removal for the mentioned aerogels, we intend to use an unsupervised machine learning approach known as "Principal Component Analysis". PCA showed that the chitosan-based ones have the lowest regeneration efficiencies, along with a moderate number of regenerations. NC2, NC9, and G5 are preferred where there is high adsorption energy to the membrane, and high porosities could be tolerated, but this allows lower removal efficiencies of dye contaminants. NC3, NC5, NC6, and NC11 have high removal efficiencies even with low porosities and surface area. In brief, PCA presents a powerful tool to unravel the efficiency of aerogels towards dye removal. Hence, several conditions need to be considered when employing or even manufacturing the investigated aerogels.

Application of Unsupervised Learning for the Evaluation of Burial Behavior of Geomaterials in Peatlands: Case of Lignin Moieties Yielded by Alkaline Oxidative Cleavage.

Tropical Peatlands accumulate organic matter (OM) and a significant source of carbon dioxide (CO2) and methane (CH4) under anoxic conditions. However, it is still ambiguous where in the peat profile these OM and gases are produced. The composition of organic macromolecules that are present in peatland ecosystems are mainly lignin and polysaccharides. As greater concentrations of lignin are found to be strongly related to the high CO2 and CH4 concentrations under anoxic conditions in the surface peat, the need to study the degradation of lignin under anoxic and oxic conditions has emerged. In this study, we found that the "Wet Chemical Degradation" approach is the most preferable and qualified to evaluate the lignin degradation in soils accurately. Then, we applied PCA for the molecular fingerprint consisting of 11 major phenolic sub-units produced by alkaline oxidation using cupric oxide (II) along with alkaline hydrolysis of the lignin sample presented in the investigated peat column called "Sagnes". The development of various characteristic indicators for lignin degradation state on the basis of the relative distribution of lignin phenols was measured by chromatography after CuO-NaOH oxidation. In order to achieve this aim, the so-called Principal Component Analysis (PCA) has been applied for the molecular fingerprint composed of the phenolic sub-units, yielded by CuO-NaOH oxidation. This approach aims to seek the efficiency of the already available proxies and potentially create new ones for the investigation of lignin burial along a peatland. Lignin phenol vegetation index (LPVI) is used for comparison. LPVI showed a higher correlation with PC1 rather than PC2. This confirms the potential of the application of LPVI to decipher vegetation change, even in a dynamic system as the peatland. The population is composed of the depth peat samples, and the variables are the proxies and relative contributions of the 11 yielded phenolic sub-units.

Metal Oxide Hydrogel Composites for Remediation of Dye-Contaminated Wastewater: Principal Component Analysis.

Water pollution is caused by multiple factors, such as industrial dye wastewater. Dye-contaminated water can be treated using hydrogels as adsorbent materials. Recently, composite hydrogels containing metal oxide nanoparticles (MONPs) have been used extensively in wastewater remediation. In this study, we use a statistical and artificial intelligence method, based on principal component analysis (PCA) with different applied parameters, to evaluate the adsorption efficiency of 27 different MONP composite hydrogels for wastewater dye treatment. PCA showed that the hydrogel composites CTS@Fe3O4, PAAm/TiO2, and PEGDMA-rGO/Fe3O4@cellulose should be used in situations involving high pH, time to reach equilibrium, and adsorption capacity. However, as the composites PAAm-co-AAc/TiO2, PVPA/Fe3O4@SiO2, PMOA/ATP/Fe3O4, and PVPA/Fe3O4@SiO2, are preferred when all physical and chemical properties investigated have low magnitudes. To conclude, PCA is a strong method for highlighting the essential factors affecting hydrogel composite selection for dye-contaminated water treatment.

Entropy Generation and Statistical Analysis of MHD Hybrid Nanofluid Unsteady Squeezing Flow between Two Parallel Rotating Plates with Activation Energy.

Squeezing flow is a flow where the material is squeezed out or disfigured within two parallel plates. Such flow is beneficial in various fields, for instance, in welding engineering and rheometry. The current study investigates the squeezing flow of a hybrid nanofluid (propylene glycol-water mixture combined with paraffin wax-sand) between two parallel plates with activation energy and entropy generation. The governing equations are converted into ordinary differential equations using appropriate similarity transformations. The shooting strategy (combined with Runge-Kutta fourth order method) is applied to solve these transformed equations. The results of the conducted parametric study are explained and revealed in graphs. This study uses a statistical tool (correlation coefficient) to illustrate the impact of the relevant parameters on the engineering parameters of interest, such as the surface friction factor at both plates. This study concludes that the squeezing number intensifies the velocity profiles, and the rotating parameter decreases the fluid velocity. In addition, the magnetic field, rotation parameter, and nanoparticle volumetric parameter have a strong negative relationship with the friction factor at the lower plate. Furthermore, heat source has a strong negative relationship with heat transfer rate near the lower plate, and a strong positive correlation with the same phenomena near the upper plate. In conclusion, the current study reveals that the entropy generation is increased with the Brinkman number and reduced with the squeezing parameter. Moreover, the results of the current study verify and show a decent agreement with the data from earlier published research outcomes.

Evaluation of the Adsorption Efficiency of Graphene Oxide Hydrogels in Wastewater Dye Removal: Application of Principal Component Analysis.

Industrial dye wastewater is one of the major water pollution problems. Adsorbent materials are promising strategies for the removal of water dye contaminants. Herein, we provide a statistical and artificial intelligence study to evaluate the adsorption efficiency of graphene oxide-based hydrogels in wastewater dye removal by applying Principal Component Analysis (PCA). This study aims to assess the adsorption quality of 35 different hydrogels. We adopted different approaches and showed the pros and cons of each one of them. PCA showed that alginate graphene oxide-based hydrogel (without polyvinyl alcohol) had better tolerance in a basic medium and provided higher adsorption capacity. Polyvinyl alcohol sulfonated graphene oxide-based hydrogels are suitable when higher adsorbent doses are required. In conclusion, PCA represents a robust way to delineate factors affecting hydrogel selection for pollutant removal from aqueous solutions.

BH3-Only Proteins Noxa and Puma Are Key Regulators of Induced Apoptosis.

Apoptosis is an evolutionarily conserved and tightly regulated cell death pathway. Physiological cell death is important for maintaining homeostasis and optimal biological conditions by continuous elimination of undesired or superfluous cells. The BH3-only pro-apoptotic members are strong inducers of apoptosis. The pro-apoptotic BH3-only protein Noxa activates multiple death pathways by inhibiting the anti-apoptotic Bcl-2 family protein, Mcl-1, and other protein members leading to Bax and Bak activation and MOMP. On the other hand, Puma is induced by p53-dependent and p53-independent apoptotic stimuli in several cancer cell lines. Moreover, this protein is involved in several physiological and pathological processes, such as immunity, cancer, and neurodegenerative diseases. Future heat shock research could disclose the effect of hyperthermia on both Noxa and BH3-only proteins. This suggests post-transcriptional mechanisms controlling the translation of both Puma and Noxa mRNA in heat-shocked cells. This study was also the chance to recapitulate the different reactional mechanisms investigated for caspases.

Assessment of the Efficiency of Chemical and Thermochemical Depolymerization Methods for Lignin Valorization: Principal Component Analysis (PCA) Approach.

Energy demand and the use of commodity consumer products, such as chemicals, plastics, and transportation fuels, are growing nowadays. These products, which are mainly derived from fossil resources and contribute to environmental pollution and CO2 emissions, will be used up eventually. Therefore, a renewable inexhaustible energy source is required. Plant biomass resources can be used as a suitable alternative source due to their green, clean attributes and low carbon emissions. Lignin is a class of complex aromatic polymers. It is highly abundant and a major constituent in the structural cell walls of all higher vascular land plants. Lignin can be used as an alternative source for fine chemicals and raw material for biofuel production. There are many chemical processes that can be potentially utilized to increase the degradation rate of lignin into biofuels or value-added chemicals. In this study, two lignin degradation methods, CuO-NaOH oxidation and tetramethyl ammonium hydroxide (TMAH) thermochemolysis, will be addressed. Both methods showed a high capacity to produce a large molecular dataset, resulting in tedious and time-consuming data analysis. To overcome this issue, an unsupervised machine learning technique called principal component analysis (PCA) is implemented.

The Application of Principal Component Analysis (PCA) for the Optimization of the Conditions of Fabrication of Electrospun Nanofibrous Membrane for Desalination and Ion Removal.

Nowadays, acquiring a water supply for urban and industrial uses is one of the greatest challenges facing humanity for ensuring sustainability. Membrane technology has been considered cost-effective, encompasses lower energy requirements, and at the same time, offers acceptable performance. Electrospun nanofibrous membranes (ENMs) are considered a novel and promising strategy for the production of membranes that could be applied in several treatment processes, especially desalination and ion removal. In this study, we apply an unsupervised machine-learning strategy, the so-called principal component analysis (PCA), for the purpose of seeking discrepancies and similarities between different ENMs. The main purpose was to investigate the influence of membrane fabrication conditions, characteristics, and process conditions in order to seek the relevance of the application of different electrospun nanofibrous membranes (ENMs). Membranes were majorly classified into single polymers/layers, from one side, and dual multiple layer ENMs, from another side. For both classes, variables related to membrane fabrication conditions were not separated from membrane characterization variables. This reveals that membranes' characteristics not only depend on the chemical composition, but also on the fabrication conditions. On the other hand, the process conditions of ENM fabrication showed an extensive effect on membranes' performance.

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study.

Thermal performance of energy conversion systems is one of the most important goals to improve the system's efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in addition to dynamic viscosity are the properties that dramatically affect heat transfer characteristics. These features of hybrid nanofluids, as promising heat transfer fluids, are influenced by different constituents, including volume fraction, size of solid parts and temperature. In this article, the mentioned features of the nanofluids with hybrid nanostructures and the proposed models for these properties are reviewed. It is concluded that the increase in the volume fraction of solids causes improvement in thermal conductivity and dynamic viscosity, while the trend of variations in the specific heat depends on the base fluid. In addition, the increase in temperature increases the thermal conductivity while it decreases the dynamic viscosity. Moreover, as stated by the reviewed works, different approaches have applicability for modeling these properties with high accuracy, while intelligent algorithms, including artificial neural networks, are able to reach a higher precision compared with the correlations. In addition to the used method, some other factors, such as the model architecture, influence the reliability and exactness of the proposed models.

CDK5: Key Regulator of Apoptosis and Cell Survival.

The atypical cyclin-dependent kinase 5 (CDK5) is considered as a neuron-specific kinase that plays important roles in many cellular functions including cell motility and survival. The activation of CDK5 is dependent on interaction with its activator p35, p39, or p25. These activators share a CDK5-binding domain and form a tertiary structure similar to that of cyclins. Upon activation, CDK5/p35 complexes localize primarily in the plasma membrane, cytosol, and perinuclear region. Although other CDKs are activated by cyclins, binding of cyclin D and E showed no effect on CDK5 activation. However, it has been shown that CDK5 can be activated by cyclin I, which results in anti-apoptotic functions due to the increased expression of Bcl-2 family proteins. Treatment with the CDK5 inhibitor roscovitine sensitizes cells to heat-induced apoptosis and its phosphorylation, which results in prevention of the apoptotic protein functions. Here, we highlight the regulatory mechanisms of CDK5 and its roles in cellular processes such as gene regulation, cell survival, and apoptosis.

Hydrophobic Effect of Alkyl Groups Stabilizing Self-Assembled Colloids in Water.

Self-assembling behavior of hydrophobic molecules in water confirms that the hydrophobic effect (HE) of alkyl groups stabilizes the aqueous colloids. Fe(CO){CO(CH2)n-1CH3}(Cp)(PPh3) molecules (FpCn) with a Fp head and alkyl tail are water-insoluble. FpC1 aggregates in water precipitated in hours, whereas the hydrophobic interaction (HI) of long alkyl groups drove FpCn (n = 6-18) molecules assemble into colloids in water. The alkyl tails interacted and stretched as indicated by IR analysis, while the separation of Fp head groups at the surface of colloids was indicated by the redox behavior of Fe elements. The molecules therefore arranged into a proposed "Y" shape with hydration cavities. The IR and cyclic voltammetry (CV) analyses indicated that the longer the alkyl tail, the larger the hydration cavity and the stronger the HI. Both HI and hydrophobic hydration (HH) were identified as the HE stabilizing the colloids in water. This knowledge is applicable for a broad range of disciplines, including biology, chemistry, and material science.

Iron-Carbonyl Aqueous Vesicles (MCsomes) by Hydration of [Fe(CO){CO(CH2)5CH3}(Cp)(PPh3)] (FpC6): Highly Integrated Colloids with Aggregation-Induced Self-Enhanced IR Absorption (AI-SEIRA).

Self-assembly of hydrophobic molecules into aqueous colloids contradicts common chemical intuition, but has been achieved through hydration of [Fe(CO){CO(CH2)5CH3}(Cp)(PPh3)] (FpC6). FpC6 has no surface activity, no NMR signals in D2O and no critical aggregation concentration (CAC) in H2O. The molecule, however, contains both acyl and terminal CO groups that are prone to being hydrated. By adding water to a solution in THF, self-assembly of FpC6 can be initiated through water-carbonyl interactions (WCIs) with the highly polarized acyl CO groups. This aggregation subsequently enhances the hydration of the acyl CO groups and also induces the WCI of otherwise unhydrated terminal CO groups. The resultant metal-carbonyl aggregates have been proved to be bilayer vesicles with iron complexes exposed towards water and alkyl chains forming inner walls (MCsomes). These MCsomes show high structure integration upon dilution due to the hydrophobic nature of the building blocks. The highly polarized CO groups on the surface of the MCsomes result in a negative zeta potential (-65 mV) and create a local electric field, which significantly enhances the IR absorption of CO groups by more than 100-fold. This is the first discovery of aggregation-induced self-enhanced IR absorption (AI-SRIRA) without the assistant of external dielectric substrates. Highly integrated MCsomes are, therefore, promising as a novel group of materials, for example, for IR-based sensing and imaging.

Synthesis of main-chain metal carbonyl organometallic macromolecules (MCMCOMs).

Synthesis of main-chain metal carbonyl organometallic macromolecules (MCMCOMs) is difficult, mainly due to the instability of metal carbonyl complexes. Despite its challenge a number of MCMCOMs has been prepared by strategically using organometallic, organic, and polymer synthetic chemistry. Main contributions to this research field were reported by the groups of Tyler, Pannell, and Wang and are briefly summarized in this article.

Role of poly(vinylpyrrolidone) (PVP) and other sterically protecting polymers in selective stabilization of {111} and {100} facets in pentagonally twinned silver nanoparticles.

The role of PVP in the selective formation of pentagonally twinned silver nanoparticle morphologies and the corresponding selective facet stabilization is elucidated. Both for decahedral and pentagonal rod nanoparticles, PVP does not promote specific facet selection but is instrumental in colloidal stabilization and facet preservation through steric protection.

Gold plating of silver nanoparticles for superior stability and preserved plasmonic and sensing properties.

Gold-plated silver nanoparticles (Au@AgNPs, shell@core) with high stability in various environments detrimental to AgNPs have been produced with preserved plasmonic and sensing properties.