Uchechukwu C. Wejinya

Determination of mechanical properties of soft tissue scaffolds by atomic force microscopy nanoindentation

While the determination of mechanical properties of a hard scaffold is relatively straightforward, the mechanical testing of a soft tissue scaffold poses significant challenges due in part to its fragility. Here, we report a new approach for characterizing the stiffness and elastic modulus of a soft scaffold through atomic force microscopy (AFM) nanoindentation. Using collagen-chitosan hydrogel scaffolds as model soft tissue scaffolds, we demonstrated the feasibility of using AFM nanoindentation to determine a force curve of a soft tissue scaffold. A mathematical model was developed to ascertain the stiffness and elastic modulus of a scaffold from its force curve obtained under different conditions. The elastic modulus of a collagen-chitosan (80%/20%, v/v) scaffold is found to be 3.69 kPa. The scaffold becomes stiffer if it contains more chitosan. The elastic modulus of a scaffold composed of 70% collagen and 30% chitosan is about 11.6 kPa. Furthermore, the stiffness of the scaffold is found to be altered significantly by extracellular matrix deposited from cells that are grown inside the scaffold. The elastic modulus of collagen-chitosan scaffolds increased from 10.5 kPa on day 3 to 63.4 kPa on day 10 when human foreskin fibroblast cells grew inside the scaffolds. Data acquired from these measurements will offer new insights into understanding cell fate regulation induced by physiochemical cues of tissue scaffolds.

Adaptable End Effector for Atomic Force Microscopy Based Nanomanipulation

Nanomanipulation using the atomic force microscope (AFM) has been extensively investigated for many years. But the efficiency and accuracy of AFM-based nanomanipulation are still major issues due to the nonlinearities and uncertainties in nanomanipulation operations. The deformation of the cantilever caused by manipulation force is one of the most major nonlinearities and uncertainties. It causes difficulties in accurately controlling the tip position, and results in missing the position of the object. The softness of the conventional cantilevers also causes the failure of manipulation of sticky nano-objects because the tip can easily slip over the nano-objects. In this paper, an active atomic force microscopy probe is used as an adaptable end effector to solve these problems by actively controlling the cantilevers flexibility or rigidity during nanomanipulation. A control voltage is applied to the piezo layer of the adaptable end effector to exert a reverse bending moment on the cantilever to balance the bending moment caused by the interaction force during manipulation. Thus, the adaptable end effector is controlled to maintain straight shape during manipulation. A detailed model of the adaptable end effector is presented in the paper. Control of the adaptable end effector employing an optimal LQR control law is derived and implemented. The experimental results verify the validity of the model and effectiveness of the controller. The nanomanipulation results also prove the increased efficiency of AFM-based nanomanipulation using the adaptable end effector

Design, Manufacturing, and Testing of Single-Carbon-Nanotube-Based Infrared Sensors

As a 1-D nanostructural material, carbon nanotube (CNT) has attracted lot of attention and has been used to build various nanoelectronic devices due to its unique electronic properties. In this paper, a reliable and efficient nanomanufacturing process was developed for building single-CNT-based nanodevices by depositing the CNTs on the substrate surface and then aligning them to bridge the electrode gap using the atomic force microscopy (AFM) based nanomanipulation. With this technology, single CNT-based IR sensors have been fabricated for investigating CNTs electronic and photonic properties. The fabrication of single-CNT-based IR sensors demonstrated the reliability and efficiency of the nanomanufacturing process. Experimental tests on single-multiwalled-CNT-based IR sensors have shown much larger photocurrent and quantum efficiency than other reported studies. It has also been shown that a high signal to dark current ratio can be accomplished by single-walled-CNT (SWNT) based IR sensors. Moreover, the testing of SWNT-bundle-based IR sensors verified that the performance of CNT bundle/film-based nanoelectronic devices was limited by the mixing of semiconducting CNTs and metallic CNTs, as well as the unstable CNT-CNT junctions in a CNT bundle or network.

Photovoltaic effect in single carbon nanotube-based Schottky diodes

Photovoltaic effects in individual single-walled carbon nanotube (SWCNT) based Schottky diodes were investigated for infrared detection in this paper. Different contact conditions (symmetric and asymmetric CNT-metal contacts) have been studied for optimising the performance of SWCNT-based infrared detector. Experiments demonstrated that the asymmetric structure could improve the photodiode performance by increasing the signal-to-dark current ratio up to two orders of magnitude higher than a symmetric device. With the perfect photodiode I-V characteristic curves, SWCNTs show a strong potential of applications in solar collection, infrared sensing and nanogenerators.

Automated Nanomanufacturing System to Assemble Carbon Nanotube Based Devices

In this paper we report the design and implementation of a novel automated manufacturing system for carbon nanotube (CNT)-based nanodevices, which integrates a new dielectrophoretic (DEP) microchamber into a robotic-based deposition workstation. The microchamber has been fabricated to separate and select CNTs with the desired electronic property by using DEP force. Moreover, a series of tools for mass-producing consistent nanodevices has been developed with the CNT deposition workstation, such as computer-controllable micromanipulators and a micro-active nozzle. Detailed experimental studies of the CNT separation and deposition processes have been performed on both single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs). Preliminary results show that CNTs could be manipulated to multiple pairs of microelectrodes repeatedly. Consistent I—V characteristics and CNT formations of the fabricated devices were obtained. The yield of semi-conducting CNTs was also increased by using our system. Therefore, by using the proposed CNT separation and deposition system, CNT-based nanodevices with specific and consistent electronic properties can be manufactured automatically and effectively.

Closed-loop optimal control-enabled piezoelectric microforce sensors

This paper presents a closed-loop optimally controlled force-sensing technology with applications in both micromanipulation and microassembly. The microforce-sensing technology in this paper is based on a cantilevered composite beam structure with embedded piezoelectric polyvinylidene fluoride (PVDF) actuating and sensing layers. In this type of sensor, the application of an external load causes deformation within the PVDF sensing layer. This generates a signal that is fed through a linear quadratic regulator (LQR) optimal servoed controller to the PVDF actuating layer. This in turn generates a balancing force to counteract the externally applied load. As a result, a closed feedback loop is formed, which causes the tip of this highly sensitive sensor to remain in its equilibrium position, even in the presence of dynamically applied external loads. The sensors stiffness is virtually improved as a result of the equilibrium position whenever the control loop is active, thereby enabling accurate motion control of the sensor tip for fine micromanipulation and microassembly. Furthermore, the applied force can be determined in real time through measurement of the balance force

Cutting forces related with lattice orientations of graphene using an atomic force microscopy based nanorobot

The relationship between cutting forces and lattice orientations of monolayer graphene is investigated by using an atomic force microscopy (AFM) based nanorobot. In the beginning, the atomic resolution image of the graphene lattice is obtained by using an AFM. Then, graphene cutting experiments are performed with sample rotation method, which gets rid of the tip effect completely. The experimental results show that the cutting force along the armchair orientation is larger than the force along the zigzag orientation, and the cutting forces are almost identical every 60 degrees, which corresponds well with the 60 degrees symmetry in graphene honeycomb lattice structure. By using Poisson analysis method, the single cutting force along zigzag orientation is 3.9 nN, and the force along armchair is 20.5 nN. This work lays the experimental foundation to build a close-loop fabrication strategy with real-time force as a feedback sensor to control the cutting direction

High sensitivity 2-D force sensor for assembly of surface MEMS devices

This paper aims at advancing micromanipulation technology with in situ polyvinylidene fluoride (PVDF) piezoelectric force sensing during microassembly and packaging process. Based on the previously developed PVDF 1-D sensor, by employing the parallel beam structure, a novel 2-D force sensor with relatively high natural frequency and sensitivity is optimally designed. The sensor can detect micro force and force rate signals, which can be fed back so as to greatly, improve the reliability of microassembly. Preliminary calibration and experimental results on assembly of surface MEMS devices both verified the performance of the new 2-D sensor that demonstrates a high sensitivity and a resolution in the range of /spl mu/N. Ultimately the technology would provide a critical and major step towards the development of automated micro-manufacturing processes for batch assembly of micro devices.

Real-time written-character recognition using MEMS motion sensors: Calibration and experimental results

A Micro Inertial Measurement Unit (μIMU) based on Micro Electro Mechanical Systems (MEMS) sensor is applied to sense the motion information produced by human subjects. The μIMU is built with three-dimensional accelerometer. During our experiments, although we write the characters in a plane, all the three-dimensional acceleration information is taken from μIMU in processing data as the third dimension is very helpful to be applied in practical application. In our previous work, the effectiveness of different data processing methods including Fast Fourier Transform (FFT) and Discrete Cosine Transform (DCT) are compared, thus the latter, which is better, is adopted in this paper. Also, Hidden Markov Models (HMM) is introduced and used as a tool to realize hand gesture classification. With this method, new experimental results of hand-written recognition are obtained and stated in this paper. Five Arabic numbers, 0–4, are written forty times by two different persons and we utilize all the data as training samples. Then when another new 29 samples are input to the network for recognition, we obtain a high correct rate at 93%. Ultimately, this technology will provide the feasibility of character recognition and potential for humangesture recognition.

Artificial intelligence for forest fire prediction

Forest fire prediction constitutes a significant component of forest fire management. It plays a major role in resource allocation, mitigation and recovery efforts. This paper presents a description and analysis of forest fire prediction methods based on artificial intelligence. A novel forest fire risk prediction algorithm, based on support vector machines, is presented. The algorithm depends on previous weather conditions in order to predict the fire hazard level of a day. The implementation of the algorithm using data from Lebanon demonstrated its ability to accurately predict the hazard of fire occurrence.