Adsorption, orientation and thermal decomposition of copper(II) hexafluoroacetylacetonate on rutile TiO2(110)

We have investigated the adsorption and thermal decomposition of copper hexafluoroacetylacetonate (Cu-11(hfaC)(2)) on single crystal rutile TiO2(110). Low energy electron diffraction shows that room temperature saturation coverage of the Cu-II(hfac)(2) adsorbate forms an ordered (2 x 1) over-layer. X-ray and ultra-violet photoemission spectroscopy of the saturated surface were recorded as the sample was annealed in a sequential manner to reveal decomposition pathways. The results show that the molecule dissociatively adsorbs by detachment of one of the two ligands to form hfac and Cu-1(hfac) which chemisorb to the substrate at 298 K. These ligands only begin to decompose once the surface temperature exceeds 473 K where Cu core level shifts indicate metallisation. This reduction from Cu(I) to Cu(0) takes place in the absence of an external reducing agent and without disproportionation and is accompanied by the onset of decomposition of the hfac ligands. Finally, C K-edge near edge X-ray absorption fine structure experiments indicate that both the ligands adsorb aligned in the < 001 > direction and we propose a model in which the hfac ligands adsorb on the 5-fold coordinated Ti atoms and the Cu-1(hfac) moiety attaches to the bridging O atoms in a square planar geometry. The calculated tilt angle for these combined geometries is approximately 10 degrees to the surface normal.

Layer-by-layer electrostatic entrapment of protein molecules on superparamagnetic nanoparticle: new strategy to enhance adsorption capacity and maintain biological activity

There is a recent interest to use inorganic-based magnetic nanoparticles as a vehicle to carry biomolecules for various biophysical applications, but direct attachment of the molecules is known to alter their conformation leading to attenuation in activity. In addition, surface immobilization has been limited to monolayer coverage. It is shown that alternate depositions of negatively charged protein molecules, typically bovine serum albumin (BSA) with a positively charged aminocarbohydrate template such as glycol chitosan (GC) on magnetic iron oxide nanoparticle surface as a colloid, are carried out under pH 7.4. Circular dichroism (CD) clearly reveals that the secondary structure of the entrapped BSA sequential depositions in this manner remains totally unaltered which is in sharp contrast to previous attempts. Probing the binding properties of the entrapped BSA using small molecules (Site I and Site II drug compounds) confirms for the first time the full retention of its biological activity as compared with native BSA, which also implies the ready accessibility of the entrapped protein molecules through the porous overlayers. This work clearly suggests a new method to immobilize and store protein molecules beyond monolayer adsorption on a magnetic nanoparticle surface without much structural alteration. This may find applications in magnetic recoverable enzymes or protein delivery.

Advances in sequential data assimilation and numerical weather forecasting: an Ensemble Transform Kalman-Bucy Filter, a study on clustering in deterministic Ensemble Square Root Filters, and a test of a new time stepping scheme in an atmospheric model

This dissertation deals with aspects of sequential data assimilation (in particular ensemble Kalman filtering) and numerical weather forecasting. In the first part, the recently formulated Ensemble Kalman-Bucy (EnKBF) filter is revisited. It is shown that the previously used numerical integration scheme fails when the magnitude of the background error covariance grows beyond that of the observational error covariance in the forecast window. Therefore, we present a suitable integration scheme that handles the stiffening of the differential equations involved and doesn’t represent further computational expense. Moreover, a transform-based alternative to the EnKBF is developed: under this scheme, the operations are performed in the ensemble space instead of in the state space. Advantages of this formulation are explained. For the first time, the EnKBF is implemented in an atmospheric model. The second part of this work deals with ensemble clustering, a phenomenon that arises when performing data assimilation using of deterministic ensemble square root filters in highly nonlinear forecast models. Namely, an M-member ensemble detaches into an outlier and a cluster of M-1 members. Previous works may suggest that this issue represents a failure of EnSRFs; this work dispels that notion. It is shown that ensemble clustering can be reverted also due to nonlinear processes, in particular the alternation between nonlinear expansion and compression of the ensemble for different regions of the attractor. Some EnSRFs that use random rotations have been developed to overcome this issue; these formulations are analyzed and their advantages and disadvantages with respect to common EnSRFs are discussed. The third and last part contains the implementation of the Robert-Asselin-Williams (RAW) filter in an atmospheric model. The RAW filter is an improvement to the widely popular Robert-Asselin filter that successfully suppresses spurious computational waves while avoiding any distortion in the mean value of the function. Using statistical significance tests both at the local and field level, it is shown that the climatology of the SPEEDY model is not modified by the changed time stepping scheme; hence, no retuning of the parameterizations is required. It is found the accuracy of the medium-term forecasts is increased by using the RAW filter.