57 resultados para Bacterial Cellulose


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In recent years, society has been increasingly concerned with bacteria that are no longer susceptible to commercial antibiotics. Faced with a lack of tools, medical practitioners today are forced to prescribe medicines that, although effective, cause as much harm to the patient as the principal infection. The purpose of this research project is to develop novel antibacterials that remain potent against bacterial infections without being toxic to the patient.

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Poor reproducibility limits the wide uptake of low-cost surface enhanced Raman spectroscopy (SERS) substrates. This study reports a relatively low-cost and reproducible cellulose nanofibre (CNF) textured SERS substrate. Utilizing a layer of CNFs deposited onto glass slides, nanoscale roughness was achieved, which facilitated effective aggregation of gold nanoparticles (AuNPs) to form a novel CNF textured SERS substrate. This substrate meets the critical roughness requirements to control the distribution of AuNPs to provide 'hot spots' for SERS detection, offering significant signal enhancement. The reproducibility and accuracy of low-cost cellulosic SERS substrates were significantly improved on a model SERS molecule of 4-aminothiophenol.

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Here we show that ionic liquids (ILs), protic or aprotic in nature containing a phosphate anion, can be used as effective impregnating compounds resulting in a 50% improvement of the carbon yield of cellulose based carbon fibres and a 70 °C reduction in the onset of the depolymerization temperature. Using 13C NMR and FTIR spectra, we characterize the carbonized fibres with and without IL impregnates. The oxidative step in the formation of carbon fibres from cellulose precursors is very important in determining the final material properties, as such we examine this stage and show that the IL reduces the onset of the cellulose depolymerization temperature while improving the oxidative stability. This study highlights the ability of ILs to act as novel impregnates which can successfully reduce the formation of tar and volatile substances during carbonization of cellulose based carbon fibres resulting in an improved carbon yield and significant cost savings due to reduced maintenance and wear of equipment. This journal is

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A novel biodegradable pH- and salinity-responsive cellulose copolymer was prepared by grafting 2-(Dimethylamino) ethylmethacrylate (DMAEMA) onto bagasse cellulose in ionic liquid. The grafting polymerization was achieved in 1-butyl-3-methylimidazolium chloride ([Bmim]Cl) under microwave irradiation. Copolymers were then characterized by Fourier transform infrared spectroscopy, scanning electron microscopy, X-ray diffraction and thermo gravimetric analysis measurements. The results revealed that polymer chains had been successfully bonded to the cellulose backbone. Furthermore, the self-assembly of cellulose-g-DMAEMA copolymers at various salt concentrations and pH solution were investigated by means of swelling behavior measurement. It indicated that the copolymers presented dual pH and salinity-responsive properties. The synthetic strategy showed great potential in the modification of other cellulosic biomass to afford new biomaterials with desired properties. © 2014 Springer Science+Business Media Dordrecht.

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This paper reports the possibility of producing cellulose nanofiber from softwood pulp using a simple ball milling technique under ambient pressure and at room temperature. The effects of milling conditions including the ball-to-cellulose mass ratio, milling time, ball size and alkaline pretreatment were investigated. It was found that milling-ball size should be carefully selected for producing fibrous morphologies instead of particulates. Milling time and ball-to-cellulose mass ratio were also found important to control the fiber morphology. Alkali pre-treatment helped in weakening hydrogen bonds between cellulose fibrils and removing small particles, but with the risks of damaging the fibrous morphology. In a typical run, cellulose nanofiber with an average diameter of 100 nm was obtained using soft mechanical milling conditions using cerium-doped zirconia balls of 0.4–0.6 mm in diameter within 1.5 h without alkaline pretreatment.

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Nitrogen-functionalized carbon nanofibers (N-CNFs) were prepared by carbonizing polypyrrole (PPy)-coated cellulose NFs, which were obtained by electrospinning, deacetylation of electrospun cellulose acetate NFs, and PPy polymerization. Supercapacitor electrodes prepared from N-CNFs and a mixture of N-CNFs and Ni(OH)2 showed specific capacitances of ∼236 and ∼1045 F g(-1), respectively. An asymmetric supercapacitor was further fabricated using N-CNFs/Ni(OH)2 and N-CNFs as positive and negative electrodes. The supercapacitor device had a working voltage of 1.6 V in aqueous KOH solution (6.0 M) with an energy density as high as ∼51 (W h) kg(-1) and a maximum power density of ∼117 kW kg(-1). The device had excellent cycle lifetime, which retained ∼84% specific capacitance after 5000 cycles of cyclic voltammetry scans. N-CNFs derived from electrospun cellulose may be useful as an electrode material for development of high-performance supercapacitors and other energy storage devices.

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Blends between the widely used thermoset resin, epoxy, and the most abundant organic material, natural cellulose are demonstrated for the first time. The blending modification induced by charge transfer complexes using a room temperature ionic liquid, leads to the formation of thermally flexible thermoset materials. The blend materials containing low concentrations of cellulose were optically transparent which indicates the miscibility at these compositions. We observed the existence of intermolecular hydrogen bonding between epoxy and cellulose in the presence of the ionic liquid, leading to partial miscibility between these two polymers. The addition of cellulose improves the tensile mechanical properties of epoxy. This study reveals the use of ionic liquids as a compatible processing medium to prepare epoxy thermosets modified with natural polymers.

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The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction.