The Study of Heterogeneous Catalysts by High-Resolution Transmission Electron MicroscoDV

Advances in instrumentation have made it possible in recent years to study the microstructure of inorganic materials at atomic resolution using the technique of high-resolution electron microscopy (HREM). Details of instrumentation have been described elsewhere [l], and applications and trends for HREM have recently been reviewed [2]. Although HREM is primarily a technique for studying bulk defects, it is increasingly also being applied in the profile-imaging mode to derive information about surfaces [3]. The high spatial resolution of the electron microscope makes it a valuable tool for the characterization of heterogeneous catalysts. This is evidenced by the growing number of studies wherein electron micrographs are being used to describe the morphology of a particular catalyst. Profile imaging is proving particularly useful in this regard for following changes in surface structure as a function of treatment conditions [4].     

Autonomous robot manipulator-based exploration and mapping system for bridge maintenance

This paper presents a system for Autonomous eXploration to Build A Map (AXBAM) of an unknown, 3D complex steel bridge structure using a 6 degree-of-freedom anthropomorphic robot manipulator instrumented with a laser range scanner. The proposed algorithm considers the trade-off between the predicted environment information gain available from a sensing viewpoint and the manipulator joint angle changes required to position a sensor at that viewpoint, and then obtains collision-free paths through safe, previously explored regions. Information gathered from multiple viewpoints is fused to achieve a detailed 3D map. Experimental results show that the AXBAM system explores and builds quality maps of complex unknown regions in a consistent and timely manner.     

Estimation of a nonvisible field-of-view mobile target incorporating optical and acoustic sensors

This paper presents a nonvisible field-of-view (NFOV) target estimation approach that incorporates optical and acoustic sensors. An optical sensor can accurately localize a target in its field-of-view whereas the acoustic sensor could estimate the target location over a much larger space, but only with limited accuracy. A recursive Bayesian estimation framework where observations of the optical and acoustic sensors are probabilistically treated and fused is proposed in this paper. A technique to construct the observation likelihood when two microphones are used as the acoustic sensor is also described. The proposed technique derives and stores the interaural level difference of observations from the two microphones for different target positions in advance and constructs the likelihood through correlation. A parametric study of the proposed acoustic sensing technique in a controlled test environment, and experiments with an NFOV target in an actual indoor environment are presented to demonstrate the capability of the proposed technique.     

Probabilistic Belief Contraction

Probabilistic belief contraction has been a much neglected topic in the field of probabilistic reasoning. This is due to the difficulty in establishing a reasonable reversal of the effect of Bayesian conditionalization on a probabilistic distribution. We show that indifferent contraction, a solution proposed by Ramer to this problem through a judicious use of the principle of maximum entropy, is a probabilistic version of a full meet contraction. We then propose variations of indifferent contraction, using both the Shannon entropy measure as well as the Hartley entropy measure, with an aim to avoid excessive loss of beliefs that full meet contraction entails.     

Multistage bayesian autonomy for high‐precision operation in a large field

This paper presents a generalized multistage bayesian framework to enable an autonomous robot to complete high‐precision operations on a static target in a large field. The proposed framework consists of two multistage approaches, capable of dealing with the complexity of high‐precision operation in a large field to detect and localize the target. In the multistage localization, locations of the robot and the target are estimated sequentially when the target is far away from the robot, whereas these locations are estimated simultaneously when the target is close. A level of confidence (LOC) for each detection criterion of a sensor and the associated probability of detection (POD) of the sensor are defined to make the target detectable with different LOCs at varying distances. Differential entropies of the robot and target are used as a precision metric for evaluating the performance of the proposed approach. The proposed multistage observation and localization approaches were applied to scenarios using an unmanned ground vehicle (UGV) and an unmanned aerial vehicle (UAV). Results with the UGV in simulated environments and then real environments show the effectiveness of the proposed approaches to real‐world problems. A successful demonstration using the UAV is also presented.     

Large area synthesis of conical carbon nanotube arrays on graphite and tungsten foil substrates

Conical carbon nanotube (CCNT) arrays were synthesized over a large area of approximately 1 cm² or more on graphite and tungsten foil substrates. Experimental observations reveal that nucleation is caused by catalyst metal cluster in the initial stages, but the tapered morphology occurs due to the difference in the rates of vertical growth by attachment carbon atoms at edges of growing graphene sheets and radial growth with epitaxial nucleation of new graphene layers near bottom at the substrate. The above mechanism is supported through re-growth experiments on straight multi-walled nanotubes and growth kinetics data, which suggest a linear relationship between the growth rate and ratio of diameter to length (d/l) of CCNT.     

The interconnected CaCO3 coated SnO2 nanocrystalline dye-sensitized solar cell with superior performance

Design, Development, fabrication and investigation of the I–V characteristics of the DSSC based on interconnected 15 nm SnO₂ nanoparticles covered with a nano-scale thin layer of CaCO₃ are described. The presence of CaCO₃ has been confirmed by its characteristic XRD pattern and EDX plots. The thickness of the protective layer can be conveniently controlled by the molar ratio of SnO₂:CaCO₃ used in the preparation of the thin film and the optimum conditions for best performance of the DSSC are presented together with possible explanations for the variations observed when the molar ratio is changed. An optimum light-to-electricity conversion efficiency of 5.4% in the presence of a layer of CaCO₃ has been obtained which is 3.2 times enhancement over the cell prepared without CaCO₃. The characterization of the surface using different techniques is explained.     

Hydrolytic Condensation of Tin(IV) Alkoxide Compounds to Form Particles with Well-Defined Morphology

The sol-gel-type condensation of tin(IV) ethoxide [Sn(OEt)₄] n (where OEt is ethoxide) under basic conditions produced spherical, submicrometer-sized tin(IV) oxide (cassiterite) particles. Transmission electron microscopy and powder X-ray diffraction data indicated that the grain size was approximately 20 to 30 Å (2 to 3 nm). The mixed-metal alkoxide compound [ZnSn(OEt)₆] was hydrolyzed under analogous conditions to give either spherical or octahedral submicrometer-sized crystalline particles of ZnSn(OH)₆ depending on the solvents used. These data demonstrated that the stoichiometry of the mixed-metal alkoxide precursor was retained during condensation. Thermal treatment of ZnSn(OH)₆ resulted in crystallization of ZnSnO₃ at approximately 676°C. At neutral pH, hydrolysis of [ZnSn(OEt)₆] resulted in formation of a high surface area (261 m²/g) amorphous powder.