Nano particle behavior in Viscous fluid

Medical field have been able to advance at a very rapid pace, from advance surgeries to developing medicine to cure many diseases. However there is still many diseases which has been discovered yet we can’t find a cure, for example Cancer, HIV exedra. Due to complicity of these viruses we are still going to batle which have not have not moved forward in a right pat for these pased years. For example if we examine today treatment for cancer we will see there are only a certain percentage of cures that a medical doctor is able to achieve with such treatment “chemotherapy”, or even surgery or other kind of medical treatment. The reason behind  surgical as well as medical treatment failure has to do with the way a human cell behaves and react to such treatments .Also the complicity of a surgery at a micro level is almost impossible, because it’s easy to make an error in such level to the complicity of our human body. If we are able to cure or stop the progress of these types of viruses or disease for each cell without damaging any other organ cells and deliver the proper medica tion to these cells we will be able to achieve our goal and save many lives. Our main goal is to develop a Nano robot which will be able to carry medicine and perform certain tasks by traveling through the veins.             

We first acknowledge that in order to reach our goal we first need to Understanding the behavior of nano or micro fluid , which turned out to be very difficult. Why? There are many variable to account for, such as pressure, viscosity… To understand and deal with these variables we used Green’s functions known as Stokeslet equation. Stokeslet’s equation deals with behavior of fluid in the nano scale which describes the velocity vector field,  where the inertia forces are no longer accounted for since we are dealing with nano scale particle where  the Ronald number Re<<1.  

During the beginning of the fall semester our goal was to develop a computational program after we had a clear understanding of what Stokeslet’s equations are taking for account all the necessary boundaries. than we started to develop a program which shows the velocity changes in a 2D graph, using a one directional source flow force.  And for the past four month we worked on building a 3D program which describes the velocity at each point with the assumption of velocity moving at a one direction and only in the XZ plain. Our next thing would be to complete the 3D program during our semester brake.

Fabrication of porous nanoparticles by galvanic replacement reaction and their application as electrochemical sensors

For the past year and I half I have been working on fabricating porous metal nanoparticles and their use as electrochemical sensors.

The basic fabrication process is a galvanic replacement reaction between an aluminum nanoparticle template (commerically bought) and a variety of metal ions in solution including nickel, cobalt, silver, copper and gold.  The reaction is driven by the reduction potential difference between aluminum (more anodic metal) and one of the other metals (more passive)  this causes the aluminum to be replaced by one of the other metals.  What results is a variety of porous structures, which depends on the replacing metal (ni, co, ag, cu).  Individual discrete porous particles, 3-D “coral” networks, and larger porous particles have all been fabricated.  In order to characterize these structures I have used scanning electron microscopes coupled with energy dispersive x-ray spectroscopy, x-ray diffraction, BET specific surface areas measurements, and TEM with electron diffraction.

Using a galvanic replacement reaction to fabricate porous nanostructures is a highly used and research fabrication method.  Researchers have used a variety of templates from silver, copper, tin, cobalt with a variety of starting shapes (nanorods, nanoparticles, nanoboxes, etc).  Below is a recent article, that was published in Science at the beginning of the month that shows the fabrication of a variety of different structures

http://www.sciencemag.org/content/334/6061/1377.short

One of the main reasons that nanotechnology is highly studied is because of the increased surface area to volume ratio that nanostructures have.  With an increased surface area nanostructures have promise as catalysts and electrocatalysts.  With our porous nanoparticles and nanostructures we are increasing the surface area even more compared to their solid counterparts.  Therefore, we are looking into electrochemical sensing as a potential application.  I have done some preliminary testing using hydrogen peroxide as our testing chemical and I have plans to finish the testing in January.

Here is an article that describes the use of silver on glassy carbon electrode for the detection of hydrogen peroxide.  It is our hope that we have a better sensitivity and selectivity of our sensor.

http://www.springerlink.com/content/k13u606l3262456m/

I will update in January more on the electrochemical aspect of my research once I have more data to discuss!  If you have any questions or comments please let me know!

Electronic Nose Cone Penetrometer

I am currently working on the incorporation of an electronic nose (EN) sensor array with a cone penetrometer (CPT) for the detection of volatile subsurface contaminants.  The CPT is a subsurface exploration device capable of reaching depths of 100 feet below the ground surface, while the EN is a sensor array that can identify vapor samples based on the principle of olfaction.  The system works be putting the EN into the CPT and inserting that into the ground in a contaminated zone.  The CPT has a heated pad and semi-permeable membrane which volatilize any liquid contaminants and allow the vapor to pass through to the EN.  The sensors in the EN are each doped with a different type of metal, giving each a standard electrical resistance.  As the vapor passes through the sensors, surface reactions between the vapor and doped metal cause changes in the standard electrical resistance.  We measure these changes in resistance and use them as inputs for artificial neural networks and statistical methods which classify the contaminant. If you’d like more information, follow the MediaFire link below which will allow you to download a paper that I recently submitted to the 2012 Geocongress containing a summary of my research and findings to date.

http://www.mediafire.com/?6t3137hx51fvef3

Currently, I am working on finishing up a sensing chamber that I have built for testing the CPT.  Although the chamber is fully constructed, vapors are volatilizing from the top and affecting the sensor’s ability to reach a stable baseline before testing.  Therefore, I’ve been designing an easily removable cap that will prevent the escape of vapors.  Once this is completed I will be investigating the prospect of including a preconcentrator and GC (gas chromatography) column with the setup.  A preconcentrator will potentially increase the ability to sense smaller and smaller contaminant concentrations. The GC column will slow down the speed at which the vapor sample travels, causing contaminant vapors to arrive at the sensors at different times and slower speeds according to molecular their weight.  Thus, it provides the capability to sense multiple contaminants in one test, where they might previously have been misidentified.

If anybody has any questions, comments, or suggestions, please feel free to respond to this post or swing by my blog.  Happy Holidays!

Cheers, Zach

CENI

Within the past 100 years, internal imaging modalities have become the “eyes of medicine” for both interventional and therapeutic procedures. Presently, there are several options available to clinically image and diagnose diseases including: X-ray radiography, Computerized Tomography (CT), Positron Emission Tomography (PET), Single Photon Emission Computerized Tomography (SPECT) and Magnetic Resonance Imaging (MRI). Each of these modalities has distinct advantages and disadvantages which impact their overall use in diagnostic medicine. Several factors to consider when selecting a modality are spatial resolution, contrast and the tissue type to be imaged. While major improvements have been made in all diagnostic modalities, increasing dependency has created an ever present need to continue exploring new methods of obtaining high quality functional images. One new modality that has been recently explored is imaging with neutrons, specifically a novel technique known as Contrast Enhanced Neutron Imaging (CENI).

The fundamental concept of neutron imaging is similar to that of x-ray or photon imaging, whereby a map of a sample is created when electromagnetic radiation interacts with atoms of the object being imaged. Interactions can occur both with the nucleus of the atom and/or surrounding electrons. Effected electromagnetic radiation does not contribute to the resulting image; higher density materials attenuate more radiation than lower density materials, allowing for differentiation between materials in the resulting image. Unlike photon or x-ray imaging however, neutron imaging uses an uncharged particle which interacts only with the nuclei of the atoms of the sample and not the surrounding electrons. Because of this, neutrons have the unique ability to penetrate high density materials with more ease than other forms of electromagnetic radiation.

The ability to penetrate dense materials has established neutron imaging as standard practice for many industrial applications. The process provides high spatial resolution, 20-200μm, and is a valuable tool within the aerospace and automotive industries for inspecting for moisture, detecting corrosion and analyzing adhesive defects of seals. By applying the neutron imaging techniques already established for industrial purposes to a clinical setting it is possible that a new highly effective imaging modality could be developed. Given the unique properties of the neutron, this new modality could hypothetically provide extremely high spatial resolutions for imaging within biological material of high densities, like bone.

Investigating the feasibility of neutron imaging for internal diagnostic medicine could eventually help identify early stages of osteosarcoma, improve methods of imaging near or through metallic implants and assess problematic blood flow around aneurysm embolization coils and vascular stents.

Quadrature Mirror Filter (QMF) Banks

This filter is used in audio or signal processing to split the wideband (WB) signal into two narrowbands (NB) using analysis filters, and then these NBs are recombined using synthesis filters. Furthermore, this concept can be extended for multiple narrowbands such as 2,4,8,16…2ⁿ. Following is the block diagram of QMF banks. The output of the Synthesis Bank should be the perfect reconstruction of the input signal of the Analysis Bank.

http://www.grin.com/object/external_document.275346/eb5dec5111467fc8f293968cf9b46558_LARGE.png

These filter banks are made of lowpass, bandpass and highpass filters. I am currently using 16 NB filter banks, however, two adjacent filters overlap, so I have to use every other one. Following figure has 16 filters from 0-50 MHz, however, only 8 are visible because other 8 are omitted.

http://gk12net.uml.edu/~pgandhi/Research Blog/Research Blog QMF Banks.jpg

I will be using these filter banks at the receiver to convert a WB signal into multiple NBs, and then spectrum sensing algorithms will be applied to each NB to find out if a signal is present or not.

Stokes Flow

Reynolds number is the ratio of inertial force to the viscous force.  It is the most important dimensionless number in fluid mechanics. “Inertia” is the property of an object to remain at a constant velocity, unless an outside force acts on it. “Viscosity” is the resistance of a fluid to flow under the influence of an applied external force.

Fluid flow is described by the Navier-Stokes equation that describes the evolution of the velocity vector field. If the Reynolds number is very small, i.e. much smaller than one,  Re << 1, the inertial terms can be neglected in the Navier Stokes equation. In this form, the equation is known as the Stokes equation. Hence the Low Reynolds number flow is also called  Stokes flow.

Spectrum Sensing

Spectrum Sensing on Wireless Sensor Networks (WSN)

WSN’s are used for gathering information, these information’s are collected through a distributed sensors. There are a variety of sensors used in WSN such as sensors measure physical or environmental conditions. WSN are being developed in many applications especially in military such as detecting of missiles, in environmental measurement such identification of chemical, biological and nuclear plumps.

Sensor failure is common, sensor could be in a faded area an unable to sense the surround or it is far away from the target. Therefore a WSN could be ranging from small number to thousands of sensors and devices equipped with workstations. Sensors are connected together to form a node, a node consists from few to many sensors deliver the same type of information. Each individual sensor sends the measurement wireless through channels to the fusion center where the final decision will be made. This method should increase the accuracy of the decision sense it is coming from more than one individual measurement.

Spectrum sensing is performing measurement on selected data of the spectrum and makes a decision based on the selected data. In other words, the ability to determine the status of a channel either it is occupied or not by a primary users. The challenge is to determine which sensor provides the more accurate data, how to infuse the individual measurement at the node to make a decision.

These links are very useful:

1)      http://arri.uta.edu/acs/networks/WirelessSensorNetChap04.pdf

2)      http://csc.lsu.edu/sensor_web/final%20papers/FusionTOSN.pdf

Spectrum Holes

When looking for literature for my thesis I found this pretty cool report that a company in Vienna, VA, just outside Washington, DC made.  Here is a link:

http://www.sharedspectrum.com/wp-content/uploads/2010_0923-General-Band-Survey-30MHz-to-3GHz.pdf

EDIT: If the paper doesn’t load, use this link – http://www.sharedspectrum.com/papers/spectrum-reports/  It is the first report listed.

A short background summary of why this was done is that the FCC made a regulation that the TV white spaces can be accessed opportunistically by users who do own the license to a particular frequency band.  Each frequency band varies in usage in time and location and that’s why cognitive radios are getting such a big focus in regards to this.  Cognitive radios can change their operation specs in real time based upon what they see in the spectrum.  Since some frequencies have a very periodic usage cycle, some do not, some are hardly used, some are heavily used, the cognitive radio allows secondary users to locate the holes within the spectrum.

This large spectrum sense is neat in that it provides a cool look into the usage of a great many bands.  The plot on page 22 shows the 928-1000 MHz band and shows the frequency usage varies an incredible amount over even this small band.  While the upper half of this band is scarcely and randomly used, the first half has some very heavily used strips, as well as a chunk that shows a periodic trend.

Building maps like this are essential for secondary users.  While secondary users must sense the spectrum in a semi-continuous fashion, it is of great importance to know which bands are worth sensing, and which are not, as well as knowing the periodic trends (If any exist) and how to exploit that.

There’s a great many things within this paper, but if nothing else Figure 9 on page 10 succinctly shows the spectrum occupancy.  Certain frequencies are so underutilized that they do not even reach 20% usage, which is just perfect for opportunistic secondary users.

Strong absorption and selective thermal emission from a mid-infrared metamaterial

Applied Physics Letters has reviewed and decided to publish my most recent paper “Strong absorption and selective thermal emission from a mid-infrared metamaterial.”

Most physics, electronics, and technological advancements to date have dealt with understanding and manipulating bulk materials. That is to say, when a material is used to perform a task, it is often shaped to the specifications of the task. Metal is stretched thin to make wire, or mixed in bulk to make alloys. Tungsten is shaped and heated to give blackbody radiation for our lights. Even silicon which is cut into nanometer-scaled pieces is expected to behave as a bulk material. That means that the values of conductivity, strength, malleability, and any other physical characteristic are all gleaned from measuring large shape-irrelevant samples of the material. These are the bulk characteristics, and this has been a very effective way of shaping our world. But there are other ways.

Bulk characteristics are determined mostly by atomic structure, makeup, and molecular formations; but these aren’t the only possible structures of a material. In fact, you can make a structure within a material by using multiple bulk materials. If the structures that you make are small enough, then you can make a new material that cannot be defined by the bulk characteristics of its constituents. This is called a metamaterial, and the shape, spacing, and bulk materials of the small structures can all be used to perform new tasks. For instance, Tungsten as long been used as a light source, but when shaped into a very small 3D structure like Lincoln logs, it can be used to emit light in a different (more efficient) way that cannot be explained by the bulk material. (Nature, May 2, 2002; Lab News, May 3, 2003)

In all metamatial development, it is important to know what small means; and to understand that small is different in different systems. After all, 32nm silicon transistors probably seem awfully small, but they are still defined by bulk characteristics. Briefly, small means subwavelength. 32nm is small but not compared to the wavelength of the electron it manipulates; so the bulk electrical properties apply. When you’re making a metamaterial to manipulate light, you have to make structures that are small compared to the wavelength of light. Yellow light has a wavelength of 600nm. To make an optical metamaterial, the structures must be around that size or smaller.

My current work is to design and build such metamaterials. The structures I design are smaller than 9um in length so that the material can have the designed properties that I want for light that has a 9um wavelength. Specifically, I want to make a structure that absorbs all light of a specific wavelength. Such a bulk material does not exist in nature, so we are here, making our own.

Wavelets in Image Processing

The September/October 2009 issue of IEEE Medicine and Biology magazine is on wavelets and time-frequency analysis in biomedical signal processing. Although I had not thought about using wavelets in my ultrasound imaging project, I thought of some possibilities after reading “Microcalcification Border Characterization” by Tiago A. Docusse, Aledir S. Pereira, and Norian Marranghello in this issue.

A wavelet is a short oscillation which starts with zero amplitude, increases to a peak and goes back to zero. There are many wavelet functions. A wavelet function has to satisfy certain requirements. These are: finite energy, zero mean, real Fourier Transform values with no negative frequency components for complex wavelets.

 The wavelet transform allows the analysis of  data regarding to its frequency components. For image data, the wavelet transform results with four images. These contain: 1. low frequency data of the input image, 2. horizontal high frequency component, 3.vertical high frequency component 4. diagonal high frequency component.

In the article “Microcalcification Border Characterization”, the authors focus on high frequency components of wavelet transform outputs due to the nature of microcalcification patterns in digital mammograms. The purpose of the study is experimenting different wavelet families to detect and enhance microcalcification patterns. The study classifies the borders as “rugged”, or ”smooth”.   Microcalcifications are calcium deposits within breast tissue. They look as white speckles on mammograms. Although they are usually benign, certain patterns may indicate pre-cancer stage.