Constituent Functional Groups Research Articles

Phytochemical analysis and quantification of flavonoid content of ethyl acetate fraction from ‘Alugbati’ (Basella rubra L.) leaves

Basella rubra L. (Alugbati) is a branching, herbaceous vine with heart-shaped leaves widely grown in Asia, with the leaves used as a herb in soups and salads. Several studies have highlighted this plant’s nutritional and biological benefits, which contain various phytochemical substances, including flavonoids. Flavonoids are polyphenolic compounds known to scavenge free radicals through specific mechanisms. This study aimed to extract and quantify flavonoids from the leaves of Basella rubra L. to emphasize its potential benefits as a food or nutraceutical. The dried leaves were pulverized and macerated with 99% methanol before partitioning with ethyl acetate and water. The fractions were tested qualitatively and then analyzed using Fourier Transform Infrared Spectroscopy (FTIR) to detect the presence of flavonoids, followed by thin layer chromatographic (TLC) analysis to confirm the presence of flavonoids. A total flavonoid content (TFC) test was performed to quantify the extracted flavonoids. The positive results from the qualitative tests and the subsequent FTIR spectra reflecting the constituent bonds and functional groups validated the presence of flavonoids in the plant fractions. The aqueous and ethyl acetate fractions yielded a total flavonoid content of 2.71 ± 0.01 and 9.61 ± 0.04 mg QE/g extract, respectively. The plant's physical and environmental background, the extraction process used, and the plant part used are all potential contributors to its flavonoid content. To maximize its nutritional and therapeutic potential, further research into other plant organs, and performing other extraction and purifying procedures, is recommended.

Copolymeric hydrogels with high capacities of hydration and methylene blue adsorption in water

Copolymers based on monomethyl hydrogen itaconate (βHI) with 2-hydroxyethyl methacrylate (HEMA) and βHI with 2-hydroxypropyl methacrylate (HPMA) were prepared in different monomer molar ratios. The reactivity parameters of copolymerization (r1 and r2) were determined. The values obtained for r1 and r2 indicate that P(βHI-co-HEMA) exhibits a high tendency towards alternate copolymerization. Structural characterization of the copolymeric hydrogels was carried out using Fourier transform infrared (FTIR) and 1H NMR spectroscopies, where the characteristic signals of the constituent functional groups were observed. The typical thermal stability of the polymeric systems was observed by thermogravimetric analysis (TGA). In addition, the swelling capacity in water was evaluated, and a swelling percentage over 655 % and 1249 % presented P(βHI-co-HEMA) and P(βHI-co-HPMA) respectively, in a period of 15 days at pH 10, and presence of 0.05 M NaNO3.Additionally, methylene blue (MB) adsorption capacity was studied in batch mode by varying different experimental parameters. MB adsorption capacity of 122.79 mg g−1 and 112.24 mg g−1 were achieved for P(βHI-co-HEMA) and P(βHI-co-HPMA), respectively. The data obtained were fitted to the Elovich kinetic model and to a Redlich-Peterson isotherm. The thermodynamic parameters indicated that the adsorption phenomenon is spontaneous, endothermic and with a high probability of occurrence. The adsorption efficiency decreases by approximately 60 % in the presence of interfering ions (Na+, K+, Ca2+ and Cl−). The adsorbents are reusable and achieve high MB adsorption efficiency (90 % and 80 % for P(βHI-co-HEMA) and P(βHI-co-HPMA) respectively) in a textile-simulated wastewater. Based on the results obtained, it is concluded that P(βHI-co-HEMA) and P(βHI-co-HPMA) show great potential for use in adsorption of water molecules, for MB retention and purification of simulated effluents from the textile industry.

Transforming seafood waste: Green mussel shell-derived hydroxyapatite as a catalyst for spirooxindole synthesis

Utilizing bioresources and renewable materials represents a sustainable approach in chemical reactions. This study aims to harness seafood waste from green mussel shells as a raw material to produce hydroxyapatite (HAp) and employ it as a heterogeneous catalyst for synthesizing spirooxindole compounds. The green mussel shells underwent varying temperature calcination to yield CaO, subsequently utilized in HAp synthesis. XRD analysis exhibited the characteristic peak pattern of HAp and revealed a reduction in crystal sizes with increased calcination temperatures for CaO formation. Additionally, FTIR analysis identified the constituent functional groups of HAp in the sample. SEM analysis revealed irregularly shaped HAp particles, with average sizes of 268.42 nm (HAp-800), 212.46 nm (HAp-900), and 184.6 nm (HAp-1000). Using HAp as a catalyst in synthesizing spirooxindole yielded a 64 % product yield and demonstrated reusability. Utilizing mussel shell-derived hydroxyapatite as a catalyst offers a cost-effective and environmentally friendly approach to achieving higher product yields.

Role of Biosynthesized Silver Nanoparticles with Trigonella foenum-graecum Seeds in Wastewater Treatment

As the human population continues to escalate, its requirement for clean water is also increasing. This has resulted in an increased dependency on wastewater effluent to maintain the base flow of urban streams, especially in water-stressed regions. The present study reports the synthesis of AgNPs with green credentials using an aqueous extract of Trigonella foenum-graecum seeds. The observance of surface plasmon resonance (SPR) with UV–Vis spectrophotometry confirmed the presence of spherical/oblong particles with a mean diameter of 43.8 nm and low polydispersity index (PDI) of 0.391 measured by transmission electron microscopy (TEM) and DLS (dynamic light scattering) technique, respectively. The elemental map of AgNPs was demonstrated with energy-dispersive spectroscopy (EDS) and the constituent functional groups were identified by the FTIR spectra, which were similar to the bulk seed extract with a slight shift in the pattern. The emission spectrum of nanoparticles was recorded for the excitation wavelength of 349 using fluorescence microscopy and the crystalline structure was assessed using X-ray diffraction. The potential wastewater remedial efficacy of the synthesized AgNPs was evaluated based on the water quality parameters (pH, EC, BOD, COD) of the sewage effluent collected from a local Sewage Treatment Plant (STP). Furthermore, the photo degradative efficacy was investigated using the degradation percentage of Crystal Violet (CV) dye, which was recorded as 94.5% after 20 min. In addition, the antimicrobial activity of the NPs versus bulk seed extract was assessed against two bacterial strains, Escheria coli and Staphylococcus aureus, using the disc diffusion method. The AgNPs showed a profound modulatory effect on the water quality parameters, coupled with marked antimicrobial and photodegradative activity. Thus, the biogenically synthesized AgNPs offer a prospective potential for use in wastewater remediation strategies.

Development and Characterization of Yellow Passion Fruit Peel Flour (Passiflora edulis f. flavicarpa).

In this study, the peels of the yellow passion fruit (Passiflora edulis f. flavicarpa) were used to develop a flour that was evaluated in terms of its physicochemical, microscopic, colorimetric, and granulometric characteristics, its total phenolic compound and carotenoid contents, and its antioxidant capacity. Fourier Transform Infrared (FTIR) spectroscopy measurements were employed to investigate the constituent functional groups, compounds' chemical profiles were assessed by Paper Spray Mass Spectrometry (PS-MS), and the compound's chemical profiles were evaluated by Ultra-Performance Liquid Chromatography (UPLC). This flour presented a light color, heterogeneous granulometry, high carbohydrate, carotenoid, and total phenolic compound contents with high antioxidant capacity. Scanning Electron Microscopy (SEM) showed a particulate flour, which is supposed to contribute to its compactness. FTIR demonstrated the presence of functional groups corresponding to cellulose, hemicellulose, and lignin, constituents of insoluble dietary fiber. The PS-MS analysis suggested the presence of 22 substances, covering diverse component classes such as organic, fatty, and phenolic acids, flavonoids, sugars, quinones, phenylpropanoid glycerides terpenes, and amino acids. This research demonstrated the potential of using Passion Fruit Peel Flour (PFPF) as an ingredient for food products. The advantages of using PFPF comprise the reduction of agro-industrial waste, contribution to the development of a sustainable food system, and increment of food products' functional profile. Moreover, its high content of several bioactive compounds can benefit consumers' health.

Characterization of Natural Cellulosic Fiber Obtained from the Flower Heads of Milk Thistle (Silybum marianum) as a Potential Polymer Reinforcement Material

ABSTRACT Natural fibers are being investigated as alternatives to synthetic fibers in composite materials. In this paper, the physical, chemical, thermal, and morphological characteristics of fibers derived from milk thistle flower heads were characterized as potential polymer reinforcement material using Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD), thermogravimetric analysis (TGA), and scanning electron microscopy (SEM). The fibers were found to contain 42.9% cellulose, 21.2% hemicellulose, and a negligible lignin content. The fiber constituent functional groups were similar to those observed for other natural fibers. The crystallite size and the crystallinity index were found to be 2.23 nm and 84 ± 3%, respectively. The thermal stability of the fibers was found to be 220°C. The fibers start rapidly degrading above 290°C. Morphologically, the fibers appeared to have a nonporous surface with well-structured longitudinal parallel grooves. Cross sectionally, the fibers consisted of multiple hollow lumens interconnected with cell walls. The hollowed nature of the fibers explains the low value (0.33 ± 0.04 g mL−1) of their density. The tensile strength of the fibers was determined to be 22 ± 1 MPa. In conclusion, the fibers of the flower heads of milk thistle can be used as effective reinforcements in polymer matrices.

Putative anti-proliferative effect of Indian mustard (Brassica juncea) seed and its nano-formulation

Abstract Over the past few decades, nanotechnology has shown promising prospects in biomedicine and has a proven impact on enhancing therapeutics by facilitating drug delivery. The present study brings an amalgamation of nanoscience and “clean technology” by fabricating nature-friendly nanoparticles (NPs) sans the use of chemical surfactants using Indian mustard seed, Brassica juncea L. The as-synthesized NPs were characterized to assess their average size, crystallinity, morphology, and constituent functional groups through conventional techniques: dynamic light scattering (DLS), X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR). The NPs were crystalline in nature and exhibited a mean size of 205.5 nm (PDI of 0.437) being primarily polygonal in shape. Additionally, the therapeutic efficacy of the green NPs was evaluated based on their cytotoxic effect against two human cancer lines, MCF-7 and HepG-2. Both the NPs and the bulk seeds showed a dose-dependent cytotoxic effect. However, an assessment of the antiproliferative/cytotoxic potential of the green NPs versus the bulk seeds showed that the NPs were relatively more efficacious on both cell lines. Taken together, the mustard seed NPs could be potential nutraceuticals considering the green credential in their mode of biosynthesis.

Green synthesis of Moringa oleifera leaf nanoparticles and an assessment of their therapeutic potential

The present study centered around the prospect of bio fabricating nanoparticles using Moringa oleifera leaf and investigating their therapeutic potential with an assessment of the antioxidant status. Synthesis of stable ‘green’ nanoparticles of Moringa leaf extract is successfully reported with an extensive characterization. The average particle size and polydispersity index (PDI) of the nanoparticles obtained by Dynamic light scattering (DLS) analysis were 141.6 nm and a polydispersity index (PDI) of 0.32 respectively. The UV–visible spectroscopy (UV–vis) revealed a strong red absorbance peak at 664 nm,characteristic of chlorophyll. Electron micrographs confirmed that particles were in the nano range and were spherical with minimal agglomeration. The constituent functional groups were assessed by Fourier transformed infrared spectroscopy (FTIR). The X-ray diffractograms of reflected both the amorphous and crystalline domains.Furthermore, the phytochemical screening showed a higher phenolic/ flavonoid content of the Moringa nanoparticles (NMo) in comparison to the bulk Moringa leaf extract(Mo). These results corresponded to a potent antioxidant status of the NMo which was assessed by three complementary antioxidant assays. The therapeutic potential of the nanoparticles was evaluated based on in vitro anti-diabetic assays and cytotoxicity assay on human cancer cell lines (MCF-7 and HepG-2). The key findings suggest a more profound therapeutic efficacy of NMo than the Moringa leaf extract.

Automatic materials characterization from infrared spectra using convolutional neural networks.

Infrared spectroscopy is a ubiquitous technique used to characterize unknown materials in the form of solids, liquids, or gases by identifying the constituent functional groups of molecules through the analysis of obtained spectra. The conventional method of spectral interpretation demands the expertise of a trained spectroscopist as it is tedious and prone to error, particularly for complex molecules which have poor representation in the literature. Herein, we present a novel method for automatically identifying functional groups in molecules given the corresponding infrared spectra, which requires no recourse to database-searching, rule-based, or peak-matching methods. Our model employs convolutional neural networks that are capable of successfully classifying 37 functional groups which have been trained and tested on 50 936 infrared spectra and 30 611 unique molecules. Our approach demonstrates its practical relevance in the autonomous analytical identification of functional groups in organic molecules from infrared spectra.

Smoke point prediction of oxygenated fuels using neural networks

Smoke point (SP) is an important fuel property that characterizes the propensity of aviation jet fuels and kerosene to form soot. In the present study, an artificial neural network (ANN) model based on the artificial intelligence principle was developed to predict the SP of fuels with oxygenates (ethers and alcohols) and hydrocarbons (e.g., paraffins, olefins, naphthenes, aromatics, and their blends). An experimental dataset of 366 fuel mixtures comprising 113 pure compounds, 8 types of gasoline and diesels, and 245 fuel blends was used to improve the SP-prediction performance of the ANN model. One hundred and ten of the fuel blends used were various jet fuel surrogates collected from the literature. Experimental SP of 40 new mixtures consisting of diethyl ether, dibutyl ether, dioctyl ether, diphenyl ether, and methyl tert-butyl ether along with gasoline and diesel was measured as part of this work. The molecular composition of the fuels was expressed as the weight % of eight constituent functional groups present in the fuel. These functional groups along with two additional parameters (branching index and molecular weight) were provided as ten inputs to the model. The functional groups present in the gasoline and diesel samples were determined using high-resolution 1H nuclear magnetic resonance spectroscopy. The data were randomly split into three sets: training (70 %), validation (15 %), and test (15 %). The model was initially trained and validated simultaneously, and then; it was tested. A positive linear relation was observed between the measured and predicted SPs, as indicated by a correlation coefficient of 0.98. The mean absolute error of the predicted SP was 4.5. Results showed that the SP of the fuels depended on the aforementioned parameters that served as inputs for the model. The proposed model can be used to predict the SP of pure and blended form fuels containing the aforementioned functional groups.

Graphical Analysis of A Marine Plankton Community Reveals Spatial, Temporal, and Niche Structure of Sub-Communities

Species-rich communities are structured by environmental filtering and a multitude of associations including trophic, mutualistic, and antagonistic relationships. Graphs (networks) defined from correlations in presence or abundance data have the potential to identify this structure, but species with very high absence rates or abundances frequently near detection limits can result in biased retrieval of association graphs. Here we use graph clustering analysis to identify five sub-communities of plankton from the North Atlantic Ocean. We show how to mitigate the challenges of high absence rates and detection limits. The sub-communities are distinguished partially by their constituent functional groups: one group is dominated by diatoms and another by dinoflagellates, while the other three sub-communities are mixtures of phytoplankton and zooplankton. Diagnosing pairwise taxonomic associations and linking them to specific processes is challenging because of overlapping associations and complex graph topologies. Our approach presents a robust approach for identifying candidate associations among species through sub-community analysis and quantifying the aggregate strength of pairwise associations emerging in natural communities.

A comprehensive neural network model for predicting flash point of oxygenated fuels using a functional group approach

In the present work, artificial neural networks (ANN) has been used for developing a comprehensive model for predicting flash point (FP) of petroleum fuels containing the following oxygenated chemical classes: alcohols, ethers, aldehydes, ketones and esters. 474 pure compounds and 314 blends comprising of various compounds were used for model development. The fuels were dissembled into eleven constituent functional groups namely, paraffinic CH3, CH2 and CH groups, olefinic –CH = CH2 groups, naphthenic –CH-CH2, aromatic C-CH groups, alcoholic OH groups, ether O groups, aldehydic CHO groups, ketonic CO groups and ester COO groups. These eleven groups were treated as model inputs along with molecular weight (MW) and branching index (BI) which is a structural parameter. These 13 inputs were calculated for each of the 788 fuels to generate a dataset, which was used to train the model. Two ANN models were developed, one using Matlab and other using Keras, an interface for ANN library. GridSearchCV and RandomSearch were used to optimize the network in the Keras model. The developed models showed satisfactory results when applied against the entries in the test set which comprised 20% of the dataset that was not used for model training. The regression coefficient for the comparison between the experimental and predicted data was found to be 0.981 (Matlab model) and 0.979 (Keras model). The developed models have low mean absolute errors of 3.12 K (Matlab model) and 3.55 K (Keras model) and can be used to predict (and screen) FP’s of various complex oxygenated compounds and their mixtures.

Sintesis dan Karakterisasi Hidrogel PVA/Alginat Mengandung Ekstrak Lada sebagai Pembalut Luka Antibakteri

Wound dressings act as wound protectors as well as drug delivery for wound healing. This article develops a PVA/alginate-based wound dressing material added with pepper extract to obtain its antibacterial properties. The sample preparation process consisted of several stages: the preparation of pepper extract by maceration method with ethanol solvent, preparation of PVA/Alginate hydrogel and addition of pepper extract, and testing. Antibacterial test using the paper disc method with the pathogen Staphyloccocus aureus showed that the extract had powerful antibacterial properties with an inhibition zone of about 22 m. Based on the FTIR analysis, it is known that the FTIR transmittance pattern of the PVA/alginate composite added with pepper is related to its constituent functional groups. The highest degree of swelling of the hydrogels prepared in this study was 188%.

Insights from principal component analysis applied to Py-GCMS study of Indian coals and their solvent extracted clean coal products

The present work aims at studying five Indian coals and their solvent extracted clean coal products using Py-GCMS analysis and correlating the characterization data using theoretical principal component analysis. The pyrolysis products of the original coals and the super clean coals were classified as mono-, di- and tri-aromatics, while other prominent products that were obtained included cycloalkanes, n-alkanes, and alkenes ranging from C10–C29. The principal component analysis is a dimensionality reduction technique that reduced the number of input variables in the characterization dataset and gave inferences on the relative composition of constituent compounds and functional groups and structural insights based on scores and loading plots which were consistent with the experimental observations. ATR-FTIR studies confirmed the reduced concentration of ash in the super clean coals and the presence of aromatics. The Py-GCMS data and the ATR-FTIR spectra led to the conclusion that the super clean coals behaved similarly for both coking and non-coking coals with high aromatic concentrations as compared to the raw coal. Neyveli lignite super clean coal was found to show some structural similarity with the original coals, whereas the other super clean coals showed structural similarity within themselves but not with their original coal samples confirming the selective action of the e,N solvent in solubilizing the polycondensed aromatic structures in the coal samples.

Low-Refractive-Index Deep-Ultraviolet Transparent Poly(fluoroalkyl-co-methylsilsesquioxane) Resins Synthesized by Cosolvent-Free Hydrolytic Polycondensation of Organotrimethoxysilanes.

Cosolvent-free (solventless) hydrolytic polycondensation of fluoroalkyltrimethoxysilanes of linear fluoroalkyl groups of the form R = CnF2n+1C2H4 (n = 1, 4, and 8) and methyltrimethoxysilane followed by thermal curing yielded dense polymeric silsesquioxane (SQ) resins with low refractive indices and deep-ultraviolet transparency with an ultraviolet absorption edge at ∼210 nm. The refractive index at 589 nm was adjustable at ∼1.35-1.39, and the lowest value was ∼1.354 for the stiff resin and ∼1.347 for the soft resin of poly(R-co-Me-SQ) prepared at n = 8. The refractive indices of these resins were consistent with the linear combinations of molar refractivities of constituent functional groups, and there were no free-volume anomalies.

Predicting Sooting Propensity of Oxygenated Fuels Using Artificial Neural Networks

The self-learning capabilities of artificial neural networks (ANNs) from large datasets have led to their deployment in the prediction of various physical and chemical phenomena. In the present work, an ANN model was developed to predict the yield sooting index (YSI) of oxygenated fuels using the functional group approach. A total of 265 pure compounds comprising six chemical classes, namely paraffins (n and iso), olefins, naphthenes, aromatics, alcohols, and ethers, were dis-assembled into eight constituent functional groups, namely paraffinic CH3 groups, paraffinic CH2 groups, paraffinic CH groups, olefinic –CH=CH2 groups, naphthenic CH-CH2 groups, aromatic C-CH groups, alcoholic OH groups, and ether O groups. These functional groups, in addition to molecular weight and branching index, were used as inputs to develop the ANN model. A neural network with two hidden layers was used to train the model using the Levenberg–Marquardt (ML) training algorithm. The developed model was tested with 15% of the random unseen data points. A regression coefficient (R2) of 0.99 was obtained when the experimental values were compared with the predicted YSI values from the test set. An average error of 3.4% was obtained, which is less than the experimental uncertainty associated with most reported YSI measurements. The developed model can be used for YSI prediction of hydrocarbon fuels containing alcohol and ether-based oxygenates as additives with a high degree of accuracy.

Characterization of secondary organic aerosol from heated-cooking-oil emissions: evolution in composition and volatility

Abstract. Cooking emissions account for a major fraction of urban organic aerosol. It is therefore important to understand the atmospheric evolution in the physical and chemical properties of organic compounds emitted from cooking activities. In this work, we investigate the formation of secondary organic aerosol (SOA) from oxidation of gas-phase organic compounds from heated cooking oil. The chemical composition of cooking SOA is analyzed using thermal desorption–gas chromatography–mass spectrometry (TD–GC–MS). While the particle-phase composition of SOA is a highly complex mixture, we adopt a new method to achieve molecular speciation of the SOA. All the GC-elutable material is classified by the constituent functional groups, allowing us to provide a molecular description of its chemical evolution upon oxidative aging. Our results demonstrate an increase in average oxidation state (from −0.6 to −0.24) and decrease in average carbon number (from 5.2 to 4.9) with increasing photochemical aging of cooking oil, suggesting that fragmentation reactions are key processes in the oxidative aging of cooking emissions within 2 d equivalent of ambient oxidant exposure. Moreover, we estimate that aldehyde precursors from cooking emissions account for a majority of the SOA formation and oxidation products. Overall, our results provide insights into the atmospheric evolution of cooking SOA, a majority of which is derived from gas-phase oxidation of aldehydes.

Predicting evaporation dynamics of a multicomponent gasoline/ethanol droplet and spray using non-ideal vapour-liquid equilibrium models

The present work investigates the effects of more realistic evaporation models based on non-ideal vapour-liquid equilibrium (VLE) on evaporation dynamics of a multicomponent droplet and spray. To achieve this goal, the UNIFAC model [1,2] has been used to determine the activity coefficients γ, accounting for the non-ideality of vapour-liquid equilibrium. The phase diagram of an ethanol/isooctane mixture determined by using the UNIFAC model shows good agreement with measurement. For evaporation of an ethanol/isooctane droplet, two compositions, i.e. 36 and 78 vol.% of ethanol (E36 and E78), have been studied. While Raoult’s law can reasonably approximate the evaporation process of the E36 droplet, non-ideal vapour-liquid equilibrium models which take into account the activity of liquid components must be used to properly predict the evaporation of the E78 droplet. With UNIFAC, the evaporation dynamics of a two-component isooctane/ethanol spray also show good agreement with published data. Evaporation dynamics of a four-component ethanol/gasoline spray was finally investigated. A ternary mixture composed of isooctane, n-pentane and n-decane has been used as a more realistic surrogate for gasoline. It was found that the non-ideality of the evaporation model significantly affects the vapour composition during spray evaporation. Therefore, to properly predict multi and many-component droplet and spray evaporation, which largely determines spray combustion and emission dynamics, non-ideal vapour-liquid equilibrium models should be used. Compared to other approaches, the group contribution method UNIFAC shows unique advantages in that it only requires properties of constituent functional groups of each component, which is particularly useful when there is no VLE data available for binary-component subsystems of the multicomponent mixture. The extension of its use towards modelling evaporation of a many-component liquid mixture is therefore straightforward.

Characterization of pepsin-soluble collagen extracted from gourami (Osphronemus goramy) scales.

Periodontal regeneration is the anticipated result of periodontal tissue therapy. Of the many scaffold materials utilized, collagen has been shown to possess characteristics that strongly promote the periodontal regeneration process. Gourami (Osphronemusgoramy) scales can be utilized as an alternative source of collagen due to their abundant availability, the absence of risk of zoonosis, and lack of religious strictures against their use. To analyze the characteristics of collagen extracted from gourami scales. A combination of scanning electron microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), and in vitro enzymatic biodegradation analysis were employed to determine pore sizes, constituent functional groups, and the degradation rate of collagen extractedfrom gourami scales. Collagen extracted from gourami scales demonstrated an average porosity of 13.779 μm. The FTIR test results indicated Amide A, Amide B, Amide I, Amide II, and Amide III peaks. The degradation rate of extracted collagen from gourami scales was 92.00 ± 0.8165% on the first day, 95.75 ± 1.70783% on the third day and 100% on the seventh day. The pore sizes ofcollagen extracted from gouramy scalesare suitable for use as scaffold. FTIR analysis indicated that this collagen possessed a triple helix structure. Collagen extracted from gourami scales was completely degraded within seven days when treated with 0.6 g/mL collagenase solution.

Aggregation of methacrylate-based ternary biomimetic antimicrobial polymers in solution

Using detailed atomistic simulations, we explore the morphological characteristics of aggregates formed in solution phase by ternary biomimetic antimicrobial (AM) methacrylate polymers, composed of hydrophobic, charged cationic and polar functional groups and compare it with aggregate morphologies of binary methacrylate polymers, composed only of hydrophobic and charged cationic functional groups. The effect of sequence of the constituent functional groups on aggregate conformation is also studied by considering random and block sequences along the polymer backbone. Our results show that while binary polymers tend to form robust aggregates, replacing some of the hydrophobic groups with overall charge neutral polar groups weakens the aggregate considerably, leading to increased conformational fluctuations and formation of loose-packed, open aggregates, particularly in the case of random ternary polymers. Interaction energy calculations clearly suggest that the role of inclusion of polar groups in ternary polymers is two-fold: (1) to reduce possible strong local concentration of hydrophobic groups and ‘smear’ the overall hydrophobicity along the polymer backbone to increase the solubility of the polymers (2) to compensate the loss of attractive hydrophobic interactions by forming attractive electrostatic interactions with the charged groups and contribute to aggregation formation, albeit weak. Given that most of the naturally occurring AM peptides have contributions from all the three functional groups, this study elucidates the functionally tuneable role of inclusion of polar groups in the way AM agents interact with each other in solution phase, which can eventually dictate their partitioning behaviour into bacterial and mammalian membranes.