Green Corrosion Inhibitors

Corrosion is an unavoidable fact in everyday life but always receive attention to control due to its technical, economical, and esthetical importance. Corrosion inhibitors are one of the most widely used and economically viable methods protecting metals and alloys against corrosion. Typical corrosion inhibitors are bio-toxic organic compounds, which have serious issue on toxicity. Considering the toxicity of the inhibitors, there is a tremendous interest in searching for an eco-friendly, and non-toxic green corrosion inhibitor. This chapter briefly discusses the importance and different methods of corrosion inhibitors with a particular emphasis given to the discussion on the different characteristic feature of the green corrosion inhibitors reported in the literature as a comparative view of organic inhibitors

Application of Flow-Injection Spectrophotometry to Pharmaceutical and Biomedical Analyses

The discovery of new drugs, especially when many samples have to be analyzed in the minimum of time, demand the improvement or development of new analytical methods. Various techniques may be employed for this purpose. In this context, this chapter gathers the collection of paper and represents the review of past work on spectrophotometric technique coupled to a continuous flow system to determine low concentrations of several chemical species in different kinds of pharmaceutical and biological samples. A short historical background of the flow-injection analysis technique and a brief discussion of the basic principles and potential are presented. Part of this chapter is devoted to describing the sample preparation techniques, principles, and figures of merit of analytical methods. Representative applications of flow-injection spectrophotometry to pharmaceutical and biomedical analysis are also described

Understanding The Polymerization of Ethyl Cyanoacrylate in the Superglue Fuming of Latent Prints To Optimize Print Retrieval

The cyanoacrylate fuming method (CFM) is a widespread process used in forensics to make latent prints visible for detection, acquisition, and analysis. CFM is governed by the reaction of ethyl cyanoacrylate (ECA) with biological components in fingerprints, which serve as initiators for this anionic polymerization. CFM is not a well-controlled polymerization and there are different outcomes that may result from lower temperature, one of which fits the generalization of creating more ion-pair initiators. Another effect could be minimizing termination through suppressing side reactions. Alternatively, when paired with humidity, lower temperatures may cause surface condensation, decreasing the quality of the print. This work encompasses experiments in which fingerprints on glass undergo the CFM while simultaneously controlling surface temperatures and relative humidity to prevent quality degradation. The resulting fingerprints were assessed by direct mass measurements and the molecular weight analysis via gel permeation chromatography (GPC). This provides insight into the coupling effects of temperature and humidity on the cyanoacrylate fuming method at the molecular level in order to design a more effective quantitative and qualitative protocol for forensic scientists for the retrieval of latent prints. Post-treatment of fingerprints to increase contrast between the fumed print and the surface of deposition has led to the introduction of different ECA formulations. One of interest is Lumicyano which combines ECA with a fluorescent powder before fuming, decreasing the need for additional steps post-fuming which saves overall processing time of evidence. Lumicyano and Sirchie ECA (“unmodified ECA”) were used to perform CFM with different methodologies, altering surface temperature and fuming time in order to assess any variation in polymeric properties based on formulation. A plethora of surfaces can be encountered at crime scenes. The surface of fingerprint deposition can alter the procession of polymerization through the interactions with components of the deposited print or through the behavior of the surface itself could alter CFM results. Our research investigated the changes in PECA grown on fingerprints deposited on glass, poly(ethylene terephthalate) (PET), and brass to explain how fuming on glass, plastic, and metal affect the resultant polymer and consequently, the ideal parameters for fingerprint visualization

Lipidic Formulations Inspired by COVID Vaccines as Smart Coatings to Enhance Nanoparticle-Based Cancer Therapy

Recent advances in nanomedicine have led to the introduction and subsequent establishment of nanoparticles in cancer treatment and diagnosis. Nonetheless, their application is still hindered by a series of challenges related to their biocompatibility and biodistribution. In this paper, we take inspiration from the recently produced and widely spread COVID vaccines, based on the combinational use of ionizable solid lipid nanoparticles, cholesterol, PEGylated lipids, and neutral lipids able to incorporate mRNA fragments. Here, we focus on the implementation of a lipidic formulation meant to be used as a smart coating of solid-state nanoparticles. The composition of this formulation is finely tuned to ensure efficient and stable shielding of the cargo. The resulting shell is a highly customized tool that enables the possibility of further functionalizations with targeting agents, peptides, antibodies, and fluorescent moieties for future in vitro and in vivo tests and validations. Finally, as a proof of concept, zinc oxide nanoparticles doped with iron and successively coated with this lipidic formulation are tested in a pancreatic cancer cell line, BxPC-3. The results show an astonishing increase in cell viability with respect to the same uncoated nanoparticles. The preliminary results presented here pave the way towards many different therapeutic approaches based on the massive presence of highly biostable and well-tolerated nanoparticles in tumor tissues, such as sonodynamic therapy, photodynamic therapy, hyperthermia, and diagnosis by means of magnetic resonance imaging

Redefining biorefinery: the search for unconventional building blocks for materials

This review discusses different strategies for the upgrading of biomass into sustainable monomers and building blocks as scaffolds for the preparation of green polymers and materials

Development of predictive models for catalyst development

Abstract. This work was done as a part of the BioSPRINT project, which aims to improve biorefinery operations through process intensification and to replace fossil-based polymers with new bio-based products. The goal was to identify machine learned (ML) models that will accelerate the catalyst identification with high-throughput (HTP) screening methods, identify non-obvious formulations and allow catalyst tuning for different feedstock compositions. Maximum activity for conversion of complex sugar mixtures with optimal selectivity towards the key products of interest is desired. In the literature part of the thesis, ML was studied in general, where the focus was on different variable selection methods and modeling techniques, more specifically on data-driven modeling. Furthermore, modeling in catalysis was discussed with focus on ML in catalysis. Catalyst screening and selection, descriptor modeling and selection, and predictive modeling in catalysis were studied. In the experimental part, focus was on developing ML models that predict catalyst performance with relevant descriptors. Dataset for hydrogenation of 5-ethoxymethylfurfural with simple bimetal catalysts, including main metals and promoters, was used as ML model input with the addition of catalyst descriptors found in the literature. Four different responses were used in the experiments: selectivity and conversion with two different solvents. Methods used in the experimental part were discussed in detail, where data collection, preprocessing, variable selection, modeling and model validation were considered. Reference models without variable selection were first identified. Secondly, regularization algorithms were used to identify models. Finally, models with variable subsets obtained with regularization algorithms were identified. The effect of cross-validation was also studied. In general, good modeling results were obtained with boosted ensemble tree methods, support vector machine (SVM) methods and Gaussian process regression (GPR) methods. Lasso regression turned out to be the best variable selection method. Good results were obtained with the descriptors found in the literature. It was also shown, that fairly good results can be obtained with only two variables in the studied case. Promoter variables were not considered nearly as important as main metals with variable selection algorithms. Even though the modeling results were good, the variable selection methods were almost purely data-driven, and the actual relevance of the variables cannot be guaranteed. In the future work, optimization should be studied with the goal of finding catalysts that maximize catalyst performance values based on the model predictions. Also, extrapolation capabilities of the models need to be studied and improved. The studied methods can be easily implemented to other datasets. In the BioSPRINT project, experimental results related to the dehydration reaction of C5 and C6 sugars with simple metal catalysts will be obtained and used with the studied methods.Ennustavien mallien laatiminen katalyytin valmistuksen tehostamiseksi. Tiivistelmä. Tämä työ tehtiin osana BioSPRINT-projektia, jonka tavoitteena on kehittää biojalostamoiden toimintaa parantamalla niiden prosessitehokkuutta ja korvata fossiilipohjaiset polymeerit uusilla biopohjaisilla tuotteilla. Työn tavoitteena oli muodostaa koneoppimista hyödyntämällä mallit, jotka nopeuttavat optimaalisten katalyyttien löytämistä tehoseulonnan (high-throughput (HTP) screening) avulla, auttavat identifioimaan vaikeasti löydettäviä katalyyttiyhdistelmiä ja mahdollistavat katalyytin valinnan eri lähtöainekoostumuksilla. Tavoitteena on maksimoida monimutkaisten sokeriyhdisteiden konversio ja selektiivisyys halutuiksi tuotteiksi. Työn kirjallisuusosiossa perehdyttiin koneoppimiseen yleisellä tasolla, missä pääpaino oli muuttujanvalintamenetelmissä ja datapohjaisissa mallinnusmenetelmissä. Lisäksi kirjallisuusosassa tutkittiin mallinnuksen käyttöä katalyysissä, missä pääpaino oli koneoppimisen käytössä. Työssä tarkasteltiin myös katalyyttien seulontaa ja valintaa, laskennallisten muuttujien (deskriptorien) määrittelyä ja valintaa, sekä ennustavan mallinnuksen käyttöä katalyysissä. Kokeellisessa osiossa painopiste oli koneoppimista hyödyntävien mallien muodostuksessa, jotka ennustavat katalyyttien suorituskykyä oleellisilla deskriptoreilla. Data-aineistona käytettiin 5-etoksimetyylifurfuraalin hydrausreaktion tuloksia yksinkertaisilla kaksikomponenttisilla metallikatalyyteillä, jotka sisältävät päämetallin ja promoottorin. Data-aineistoa täydennettiin kirjallisuudesta löytyvillä katalyyttien deskriptoreilla ja käytettiin koneoppimista hyödyntävien mallien sisääntulona. Tutkimuksissa käytettiin neljää eri vastemuuttujaa: selektiivisyyttä ja konversiota kahdella eri liuottimella. Kokeellisessa osiossa käytetyt menetelmät käytiin läpi perusteellisesti huomioon ottaen data-aineiston keräämisen, esikäsittelyn, muuttujanvalinnan, mallinnuksen ja mallin validoinnin. Ensin referenssimallit identifioitiin. Tämän jälkeen regularisaatioalgoritmeilla suoritettiin mallinnus. Lopuksi mallinnus suoritettiin käyttämällä muuttujajoukkoja, jotka oli valittu käyttäen regularisaatioalgoritmeja. Myös ristivalidoinnin vaikutusta tutkittiin. Yleisesti hyvät mallinnustulokset saavutettiin boosted ensemble tree -tekniikalla, tukivektorikoneella ja Gaussian process -regressiolla. Lasso-menetelmä todettiin parhaaksi muuttujanvalinta-algoritmiksi. Hyvät tulokset saavutettiin kirjallisuudesta löytyvien deskriptorien avulla. Tutkimuksissa todettiin myös, että hyvät mallinnustulokset voidaan saavuttaa kyseisessä tutkimustapauksessa jopa vain kahdella muuttujalla. Päämetalleja kuvaavien muuttujien merkitsevyys todettiin paljon suuremmaksi kuin promoottorien vastaavien muuttujien. Saatavia mallinnustuloksia tarkasteltaessa täytyy huomioida, että muuttujanvalinta oli melkein täysin datapohjainen eikä muuttujien varsinaista merkitsevyyttä voida taata. Jatkossa mallien ennustuksia voidaan hyödyntää optimoinnissa, jossa tavoitteena on etsiä katalyyttiyhdistelmä, joka maksimoi katalyyttien suorituskyvyn. Myös mallin ekstrapolointikykyä täytyy tutkia ja kehittää. Tutkittavat menetelmät ovat helposti sovellettavissa myös muille samantyylisille data-aineistoille. BioSPRINT-projektista saadaan tulevaisuudessa käytettäväksi viisi- ja kuusihiilisten sokerien dehydraatioon perustuva data-aineisto yksinkertaisilla metallikatalyyteillä, jota tullaan käyttämään jatkotutkimuksissa

Reactive transport: a review of basic concepts with emphasis on biochemical processes

Reactive transport (RT) couples bio-geo-chemical reactions and transport. RT is important to understand numerous scientific questions and solve some engineering problems. RT is highly multidisciplinary, which hinders the development of a body of knowledge shared by RT modelers and developers. The goal of this paper is to review the basic conceptual issues shared by all RT problems, so as to facilitate advancement along the current frontier: biochemical reactions. To this end, we review the basic equations to indicate that chemical systems are controlled by the set of equilibrium reactions, which are easy to model, but whose rate is controlled by mixing. Since mixing is not properly represented by the standard advection-dispersion equation (ADE), we conclude that this equation is poor for RT. This leads us to review alternative transport formulations, and the methods to solve RT problems using both the ADE and alternative equations. Since equilibrium is easy, difficulties arise for kinetic reactions, which is especially true for biochemistry, where numerous challenges are open (how to represent microbial communities, impact of genomics, effect of biofilms on flow and transport, etc.). We conclude with the basic eleven conceptual issues that we consider fundamental for any conceptually sound RT effort.This work is part of grants MEDISTRAES III funded by MCIN/AEI/ PID2019-110212RB-C22 and MCIN/AEI/PID2019-110311RB-C21 and Water JPI project MARadentro (PCI2019-103603), and by the Catalan Water Agency through the project RESTORA (CA210/18/00040). IDAEA-CSIC is a Center of Excellence Severo Ochoa (Grant CEX2018-000794-S funded by MCIN/AEI/ 10.13039/501100011033).Peer ReviewedPostprint (published version

Science handbook

1995 handbook for the faculty of Scienc

Science handbook

1995 handbook for the faculty of Scienc