Silicon nanoparticle charge trapping memory cell

A charge trapping memory with 2 nm silicon nanoparticles (Si NPs) is demonstrated. A zinc oxide (ZnO) active layer is deposited by atomic layer deposition (ALD), preceded by Al₂O₃ which acts as the gate, blocking and tunneling oxide. Spin coating technique is used to deposit Si NPs across the sample between Al₂O₃ steps. The Si nanoparticle memory exhibits a threshold voltage (V_t) shift of 2.9 V at a negative programming voltage of –10 V indicating that holes are emitted from channel to charge trapping layer. The negligible measured V_t shift without the nanoparticles and the good re‐ tention of charges (>10 years) with Si NPs confirm that the Si NPs act as deep energy states within the bandgap of the Al₂O₃ layer. In order to determine the mechanism for hole emission, we study the effect of the electric field across the tunnel oxide on the magnitude and trend of the V_t shift. The V_t shift is only achieved at electric fields above 1 MV/cm. This high field indicates that tunneling is the main mechanism. More specifically, phonon‐assisted tunneling (PAT) dominates at electric fields between 1.2 MV/cm < E < 2.1 MV/cm, while Fowler–Nordheim tunneling leads at higher fields (E > 2.1 MV/cm). (© 2014 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Recent advances in the development of graphene-based surface plasmon resonance (SPR) interfaces

Surface plasmon resonance (SPR) is a powerful technique for measurement of biomolecular interactions in real-time in a label-free environment. One of the most common techniques for plasmon excitation is the Kretschmann configuration, and numerous studies of ligand–analyte interactions have been performed on surfaces functionalized with a variety of biomolecules, for example DNA, RNA, glycans, proteins, and peptides. A significant limitation of SPR is that the substrate must be a thin metal film. Post-coating of the metal thin film with a thin dielectric top layer has been reported to enhance the performance of the SPR sensor, but is highly dependent on the thickness of the upper layer and its dielectric constant. Graphene is a single-atom thin planar sheet of sp2 carbon atoms perfectly arranged in a honeycomb lattice. Graphene and graphene oxide are good supports for biomolecules because of their large surface area and rich π conjugation structure, making them suitable dielectric top layers for SPR sensing. In this paper, we review some of the key issues in the development of graphene-based SPR chips. The actual challenges of using these interfaces for studying biomolecular interactions will be discussed and the first examples of the use of graphene-on-metal SPR interfaces for biological sensing will be presented.     

Development of a metal-chelated plasmonic interface for the linking of His-peptides with a droplet-based surface plasmon resonance read-off scheme

Monolayers of metal complexes were covalently attached to the surface of lamellar SPR interfaces (Ti/Ag/a-Si(0.63)C(0.37)) for binding histidine-tagged peptides with a controlled molecular orientation. The method is based on the activation of surface acid groups with N-hydroxysuccinimide (NHS), followed by an amidation reaction with (S)-N-(5-amino-1-carboxypentyl)iminodiacetic acid (NTA). FTIR and X-ray photoelectron spectroscopy (XPS) were used to characterize each surface modification step. The NTA modified SPR interface effectively chelated Cu(2+) ions. Once loaded with metal ions, the modified SPR interface was able to bind specifically to histidine-tagged peptides. The binding process was followed by surface plasmon resonance (SPR) in a droplet based configuration. The Cu(2+)-NTA modified interface showed protein loading comparable to commercially available NTA chips based on dextran chemistry and can thus be regarded as an interesting alternative. The sensor interface can be reused several times due to the easy regeneration step using ethylenediaminetetraacetic acid (EDTA) treatment.     

Convergence analysis of the fractional decomposition method with applications to time-fractional biological population models

In this study, we present convergence analysis along with an error estimate for time-fractional biological population equation in terms of the Caputo derivative using a new technique called the fractional decomposition method (FDM). Further, we present exact solutions to four test problems of nonlinear time-fractional biological population models to show the accuracy and efficiency of the FDM. This method based on constructing series solutions in a form of rapidly convergent series with easily computable components and without the need of linearization, discretization and perturbations. The results prove that the FDM is very effective and simple for solving fractional partial differential equations in multi-dimensional spaces, special cases of which we have described in this paper.