Latest developments in innovative manufacturing to combine nanotechnology with healthcare

Nanotechnology has become increasingly important in advancing the frontiers of many key areas of healthcare, for example, drug delivery and tissue engineering. To fully harness the many benefits of nanotechnology in healthcare, innovative manufacturing is necessary to mass produce nanoparticles and nanofibers, the two major types of nanofeatures currently sought after and of immense utilitarian value in healthcare. For example, nanoparticles are a key drug delivery enabler, the structural and mechanical mimicry are important attributes of nanofiber which are increasingly used as biomimetic agents

Polymeric Nanocomposite Structures Based on Functionalized Graphene with Tunable Properties for Nervous Tissue Replacement

Electroconductive scaffolds can be a promising approach to repair conductive tissues when natural healing fails. Recently, nerve tissue engineering constructs have been widely investigated due to the challenges in creating a structure with optimized physiochemical and mechanical properties close to the native tissue. The goal of the current study was to fabricate graphene-containing polycaprolactone/gelatin/polypyrrole (PCL/gelatin/PPy) and polycaprolactone/polyglycerol-sebacate/polypyrrole (PCL/PGS/PPy) with intrinsic electrical properties through an electrospinning process. The effect of graphene on the properties of PCL/gelatin/PPy and PCL/PGS/PPy were investigated. Results demonstrated that graphene incorporation remarkably modulated the physical and mechanical properties of the scaffolds such that the electrical conductivity increased from 0.1 to 3.9 ± 0.3 S m–1 (from 0 to 3 wt % graphene) and toughness was found to be 76 MPa (PCL/gelatin/PPy 3 wt % graphene) and 143.4 MPa (PCL/PGS/PPy 3 wt % graphene). Also, the elastic moduli of the scaffolds with 0, 1, and 2 wt % graphene were reported as 210, 300, and 340 kPa in the PCL/gelatin/PPy system and 72, 85, and 92 kPa for the PCL/PGS/PPy system. A cell viability study demonstrated the noncytotoxic nature of the resultant scaffolds. The sum of the results presented in this study suggests that both PCL/gelatin/PPy/graphene and PCL/PGS/PPy/graphene compositions could be promising biomaterials for a range of conductive tissue replacement or regeneration applications

Surfactant-Mediated Epitaxial Growth of Single-Layer Graphene in an Unconventional Orientation on SiC

We report the use of a surfactant molecule during the epitaxy of graphene on SiC(0001) that leads to the growth in an unconventional orientation, namely $R0^\circ$ rotation with respect to the SiC lattice. It yields a very high-quality single-layer graphene with a uniform orientation with respect to the substrate, on the wafer scale. We find an increased quality and homogeneity compared to the approach based on the use of a pre-oriented template to induce the unconventional orientation. Using spot profile analysis low energy electron diffraction, angle-resolved photoelectron spectroscopy, and the normal incidence x-ray standing wave technique, we assess the crystalline quality and coverage of the graphene layer. Combined with the presence of a covalently-bound graphene layer in the conventional orientation underneath, our surfactant-mediated growth offers an ideal platform to prepare epitaxial twisted bilayer graphene via intercalation.Comment: 7 pages, 3 figure

Machine learning to empower electrohydrodynamic processing

Electrohydrodynamic (EHD) processes are promising healthcare fabrication technologies, as evidenced by the number of commercialised and food-and-drug administration (FDA)-approved products produced by these processes. Their ability to produce both rapidly and precisely nano-sized products provides them with a unique set of qualities that cannot be matched by other fabrication technologies. Consequently, this has stimulated the development of EHD processing to tackle other healthcare challenges. However, as with most technologies, time and resources will be needed to realise fully the potential EHD processes can offer. To address this bottleneck, researchers are adopting machine learning (ML), a subset of artificial intelligence, into their workflow. ML has already made ground-breaking advancements in the healthcare sector, and it is anticipated to do the same in the materials domain. Presently, the application of ML in fabrication technologies lags behind other sectors. To that end, this review showcases the progress made by ML for EHD workflows, demonstrating how the latter can benefit greatly from the former. In addition, we provide an introduction to the ML pipeline, to help encourage the use of ML for other EHD researchers. As discussed, the merger of ML with EHD has the potential to expedite novel discoveries and to automate the EHD workflow

Machine learning predicts electrospray particle size

Electrospraying (ES) is a state-of-the-art processing technique with the promise of achieving key nanotechnology and contemporary manufacturing needs. As a versatile technique, ES can produce particles with different sizes, morphologies, and porosities by tuning a list of experiment parameters. However, this level of precision demands an exhaustive trial-and-error approach, at high costs and heavily relies on processing expertise. The present study demonstrates how machine learning (ML) can expedite the optimization process by accurately predicting particle diameter, for both nano- and micron-sized particles. This was achieved by constructing an informative electrospraying database containing 445 records from the literature, followed by the development of predictive ML models. Feature engineering techniques were explored, where ultimately it was found that solvent physiochemical properties as the molecular representation and data with imputation provided models the highest performance. The top two models were XGBoost and Random Forest (RF), which yielded root-mean-squared errors (RMSE) of 3.91 μm and 6.19 μm evaluated by 5-fold cross-validation (CV), respectively. These models were experimentally validated in-house with different combinations of experiment parameters, where RMSE between the predicted and actual particle size was found to be 1.30 μm for the XGBoost model and 1.62 μm for the RF model. Therefore, it was concluded that data generated by the ES literature, in addition to being both cost- and material-free, can yield high-performing ML models for predicting particle size. The ML models were also consulted to determine the key processing parameters that govern particle size, where it was concluded that the models learnt similar attributes identified by scaling laws

Magnetic Resonance Spectroscopy discriminates the response to microglial stimulation of wild type and Alzheimer's disease models.

Microglia activation has emerged as a potential key factor in the pathogenesis of Alzheimers disease. Metabolite levels assessed by magnetic resonance spectroscopy (MRS) are used as markers of neuroinflammation in neurodegenerative diseases, but how they relate to microglial activation in health and chronic disease is incompletely understood. Using MRS, we monitored the brain metabolic response to lipopolysaccharides (LPS)-induced microglia activation in vivo in a transgenic mouse model of Alzheimers disease (APP/PS1) and healthy controls (wild-type (WT) littermates) over 4 hours. We assessed reactive gliosis by immunohistochemistry and correlated metabolic and histological measures. In WT mice, LPS induced a microglial phenotype consistent with activation, associated with a sustained increase in macromolecule and lipid levels (ML9). This effect was not seen in APP/PS1 mice, where LPS did not lead to a microglial response measured by histology, but induced a late increase in the putative inflammation marker myoinositol (mI) and metabolic changes in total creatine and taurine previously reported to be associated with amyloid load. We argue that ML9 and mI distinguish the response of WT and APP/PS1 mice to immune mediators. Lipid and macromolecule levels may represent a biomarker of activation of healthy microglia, while mI may not be a glial marker

APOE-ε4 synergizes with sleep disruption to accelerate Aβ deposition and Aβ-associated tau seeding and spreading

Alzheimer\u27s disease (AD) is the most common cause of dementia. The APOE-ε4 allele of the apolipoprotein E (APOE) gene is the strongest genetic risk factor for late-onset AD. The APOE genotype modulates the effect of sleep disruption on AD risk, suggesting a possible link between apoE and sleep in AD pathogenesis, which is relatively unexplored. We hypothesized that apoE modifies Aβ deposition and Aβ plaque-associated tau seeding and spreading in the form of neuritic plaque-tau (NP-tau) pathology in response to chronic sleep deprivation (SD) in an apoE isoform-dependent fashion. To test this hypothesis, we used APPPS1 mice expressing human APOE-ε3 or -ε4 with or without AD-tau injection. We found that SD in APPPS1 mice significantly increased Aβ deposition and peri-plaque NP-tau pathology in the presence of APOE4 but not APOE3. SD in APPPS1 mice significantly decreased microglial clustering around plaques and aquaporin-4 (AQP4) polarization around blood vessels in the presence of APOE4 but not APOE3. We also found that sleep-deprived APPPS1:E4 mice injected with AD-tau had significantly altered sleep behaviors compared with APPPS1:E3 mice. These findings suggest that the APOE-ε4 genotype is a critical modifier in the development of AD pathology in response to SD

Metal ion type significantly affects the morphology but not the activity of lipase-metal-phosphate nanoflowers

Enzyme–metal-ion–phosphate nanoflowers are high-surface area materials which are known to show higher activity than the constituting protein. Although the synthesis of hybrid nanoflowers has been demonstrated with a variety of proteins and reaction conditions, only di-valent metal ions have been tested to date. We expand on previous findings by testing a range of metal ions of different valence in co-presence with lipase from Burkholderia cepacia: Ag(I), Fe(II), Cu(II), Au(III). All metal ions produced colour precipitates, although the type of metal caused different precipitate morphologies under comparable reaction conditions: from nanoflowers to forests of nano-plates and crystal-like precipitates. In contrast, the type of metal ion did not appear to significantly affect the product\u27s specific enzyme activity, which remained greater than that of free lipase. This indicates that the type of metal ion and the macroscopic arrangement of the petals play a secondary role to that of the co-presence of the metal and phosphate ions in determining lipase nanoflower activity. The demonstrated ability to produce metal–phosphate-protein nanoflowers with a selection of different metals also opens the way to producing a wider range of functional, nanostructured, materials

Electrosprayed microparticles for intestinal delivery of prednisolone

Single and coaxial electrospraying was used to prepare Eudragit L100-55 polymer microparticles containing prednisolone as the active pharmaceutical ingredient. Different compositions of prednisolone and Eudragit L100-55 were used to develop five different formulations with different polymer : drug ratios. The resultant microparticles had a toroidal shape with a narrow size distribution. Prednisolone was present in an amorphous physical state, as confirmed by X-ray diffraction analysis. Dissolution studies were carried out in order to investigate the feasibility of the proposed system for site-specific release of prednisolone. The release rates were interpreted in terms of diffusion-controlled release. It was shown that utilization of pH-responsive Eudragit L100-55 could minimize the release of prednisolone in the acidic conditions of the stomach, which was followed by rapid release as the pH of the release medium was adjusted to 6.8 after the first 2 h. This is especially desirable for the treatment of conditions including inflammatory bowel disease and colon cancer

Graphene thermal infrared emitters integrated into silicon photonic waveguides

Cost-efficient and easily integrable broadband mid-infrared (mid-IR) sources would significantly enhance the application space of photonic integrated circuits (PICs). Thermal incandescent sources are superior to other common mid-IR emitters based on semiconductor materials in terms of PIC compatibility, manufacturing costs, and bandwidth. Ideal thermal emitters would radiate directly into the desired modes of the PIC waveguides via near-field coupling and would be stable at very high temperatures. Graphene is a semi-metallic two-dimensional material with comparable emissivity to thin metallic thermal emitters. It allows maximum coupling into waveguides by placing it directly into their evanescent fields. Here, we demonstrate graphene mid-IR emitters integrated with photonic waveguides that couple directly into the fundamental mode of silicon waveguides designed for a wavelength of 4,2 {\mu}m relevant for CO${_2}$ sensing. High broadband emission intensity is observed at the waveguide-integrated graphene emitter. The emission at the output grating couplers confirms successful coupling into the waveguide mode. Thermal simulations predict emitter temperatures up to 1000{\deg}C, where the blackbody radiation covers the mid-IR region. A coupling efficiency {\eta}, defined as the light emitted into the waveguide divided by the total emission, of up to 68% is estimated, superior to data published for other waveguide-integrated emitters.Comment: 24 page