https://aao-brain.com Alessandro Ascani Orsini Fri, 27 Dec 2024 08:45:54 +0000 https://aao-brain.com en https://aao-brain.com/Title Thu, 07 Nov 2024 02:44:56 +0000 Alessandro Ascani Orsini https://aao-brain.com/Title ALESSANDRO ASCANI ORSINI Hacking the brain through engineering in Nanotechnology and Neuroscience. https://aao-brain.com/About-me Thu, 07 Nov 2024 02:44:57 +0000 Alessandro Ascani Orsini https://aao-brain.com/About-me About me Welcome! First of all, welcome to my personal website! My name is Alessandro Ascani Orsini. I am from Mandello del Lario, Italy, and I am also a Canadian citizen. I have many interests, including board games, LARPs (Live Action Role Play), and travel. However, what I love most is learning new things, particularly about time travel, chaos theory, quantum physics, nanotechnology, and the brain. Who am I? &nbsp; &nbsp;I’m currently an PhD student in Electrical and Computer Engineering at Johns Hopkins University. In Spring 2023 I graduated as a Schreyer Honors Scholar at the Pennsylvania State University with a degree in Engineering Sciences and Mechanics, a minor in Neuroscience and a minor in Nanotechnology. My dream is to become a leader in Neural Engineering and Nanotechnology, merging these two fields to create nanorobots capable of curing, diagnosing, or predicting neurodegenerative diseases. Currently, my thesis in Dr. Nitish Thakor’s lab focuses on the application of a wearable in-ear device to monitor and modulate arousal, aiming to assess and improve recovery from acute brain injury. On this website, I showcase some of the projects I’ve worked on over the years. Be sure to check back from time to time, as I’ll keep updating it as the list continues to grow! If you want to get in touch I will be happy to chat with you. Below you find my email and my LinkedIn profile. *Resume upon request. &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;︎ &nbsp; &nbsp;&nbsp;︎&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;&nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; aascani2@jh.edu <img width="800" height="800" width_o="800" height_o="800" data-src="https://freight.cargo.site/t/original/i/0fbaed70e5beaf8849a48fa4ff88082d37323edebab6d53131dcbf676513977d/me.jpeg" data-mid="221309097" border="0" src="https://freight.cargo.site/w/800/i/0fbaed70e5beaf8849a48fa4ff88082d37323edebab6d53131dcbf676513977d/me.jpeg" /> <img width="768" height="1024" width_o="768" height_o="1024" data-src="https://freight.cargo.site/t/original/i/9b32dda453feb4b7d8b3a1e021490739ca86e07d3a98f3ff43da1e8f9cb183c9/me4.JPG" data-mid="221309099" border="0" data-marker-id="3" src="https://freight.cargo.site/w/768/i/9b32dda453feb4b7d8b3a1e021490739ca86e07d3a98f3ff43da1e8f9cb183c9/me4.JPG" /> <img width="960" height="1320" width_o="960" height_o="1320" data-src="https://freight.cargo.site/t/original/i/cd07f01dead0b1fbc50a9188f5ddb2cbf353997d3969f5540b7b54a92c0ab8aa/me3.jpg" data-mid="221309098" border="0" src="https://freight.cargo.site/w/960/i/cd07f01dead0b1fbc50a9188f5ddb2cbf353997d3969f5540b7b54a92c0ab8aa/me3.jpg" /> <img width="624" height="855" width_o="624" height_o="855" data-src="https://freight.cargo.site/t/original/i/6c2338adece87d9c4dd9cebbf0b0b4b35c9d5fa4e191bb06822eff5d76818e36/me2.png" data-mid="221309100" border="0" src="https://freight.cargo.site/w/624/i/6c2338adece87d9c4dd9cebbf0b0b4b35c9d5fa4e191bb06822eff5d76818e36/me2.png" /> <img width="513" height="659" width_o="513" height_o="659" data-src="https://freight.cargo.site/t/original/i/3fb06bcd29b2218574376021ddb11e9642d0fbf6e163a96b31c437b10be9cd0e/me5.png" data-mid="221309101" border="0" src="https://freight.cargo.site/w/513/i/3fb06bcd29b2218574376021ddb11e9642d0fbf6e163a96b31c437b10be9cd0e/me5.png" /> ︎︎︎Me︎︎︎My Experience so far... Since 2019, I have been working in Dr. Bruce Gluckman’s laboratory at the Center for Neural Engineering at Penn State, developing solutions to advance brain studies and creating tools for brain interfacing. In 2021, I joined Dr. James Adair’s laboratory to fabricate silica nanoparticles for biomedical applications in the brain, which were also used in a collaborative project with Dr. Gluckman. During my time in college have taken courses in Neuroscience, Solid State Physics, Nanotechnology, Electromagnetism, and Biomedical Engineering. After graduating from Penn State in 2023, I joined the Cybernetic Implantable Devices Lab at Johns Hopkins, focusing on wearable and compact novel devices for healthcare applications. In Spring 2024, I began working in the Neuroengineering and Biomedical Instrumentation Lab on wireless devices for prosthetic control and arousal monitoring. In my spare time, I work on fun MATLAB codes and side projects related to my interests to deepen and expand my knowledge. https://aao-brain.com/3D-multimodal-muscle-histology-2024-1 Fri, 27 Dec 2024 08:45:50 +0000 Alessandro Ascani Orsini https://aao-brain.com/3D-multimodal-muscle-histology-2024-1 3D multimodal muscle histology Since Spring 2024 https://aao-brain.com/Description-5 Fri, 27 Dec 2024 08:45:54 +0000 Alessandro Ascani Orsini https://aao-brain.com/Description-5 This was my first project in Dr. Thakor’s lab when I first joined as part of his lab’s work on vascularized denervated muscle targets. The objective is to visualize and analyze structural properties of the muscle obtained through histology and correlate them to electrophysiological activity recorded in vivo. Creating 3D models The histology images were processed using different levels of image processing techniques, isolating the muscle and its shape from the rest. Key features of the tissue such as Neuromuscular Junctions (NMJs) or muscle fibers were identified either manually or using a region finding algorithm. The filtered images are then stacked based on the distance at which they were cut recreating the 3D model. Features are plotted on top of the model using the coordinates extracted from each histology layer. A convex hull is finally used to recreate the shape of the muscle. <img width="2124" height="970" width_o="2124" height_o="970" data-src="https://freight.cargo.site/t/original/i/b40a269fc18a3031acbc275a2064e979fb205a080003cf06037f2942216e71ee/rat25_scale.png" data-mid="223880218" border="0" data-scale="93" src="https://freight.cargo.site/w/1000/i/b40a269fc18a3031acbc275a2064e979fb205a080003cf06037f2942216e71ee/rat25_scale.png" /> Plotted are the NMJs of the muscle. The color indicates the level of maturation of the junctions. Finding trendsOnce the features have been extracted these can be processed and analyzed in multiple ways, using voxel regions or density based analysis to identify trends across multiple muscles. In our case we used this to find reinnervation patterns in different types of muscle surgery, comparing a vascularized denervated muscle target and an intact muscle. <img width="2822" height="1258" width_o="2822" height_o="1258" data-src="https://freight.cargo.site/t/original/i/b874eba24582d0b292d4d3321ae14be4157a7ac04f17a8cf1b00d1c76645075a/Screenshot-2024-12-28-at-6.07.57PM.png" data-mid="223882274" border="0" src="https://freight.cargo.site/w/1000/i/b874eba24582d0b292d4d3321ae14be4157a7ac04f17a8cf1b00d1c76645075a/Screenshot-2024-12-28-at-6.07.57PM.png" /> Illustration of the analysis methods from feature extraction (J-K) to multi muscle distribution analysis using voxel method (L) and Density based method (M) <img width="1954" height="1692" width_o="1954" height_o="1692" data-src="https://freight.cargo.site/t/original/i/d9c02331b3a8c9ee6626e4b2a5a92b91d63293c1913719f63cb6ed5849a4a008/Screenshot-2024-12-28-at-6.10.49PM.png" data-mid="223882361" border="0" src="https://freight.cargo.site/w/1000/i/d9c02331b3a8c9ee6626e4b2a5a92b91d63293c1913719f63cb6ed5849a4a008/Screenshot-2024-12-28-at-6.10.49PM.png" /> Reconstruction and averaging of an NMJs in an intact muscle vs a reinnervated vascularized muscle target. Overlapping EMG activity Using high-density recording arrays, it is possible to measure the individual activation of a muscle’s motor units. By analyzing the resulting heatmaps and extracted features, the correlation between specific NMJs and motor units can be identified, providing insights into the relationship between structural and electrophysiological organization. This holds significant research value for understanding muscle diseases and their progression, as well as being able to understand better reinnervation patterns in muscles. This could be helpful also for prosthetic control in cases such as RPNI <img width="2822" height="1158" width_o="2822" height_o="1158" data-src="https://freight.cargo.site/t/original/i/405373bbb4dcb73d427bc1c78768d37113585267640a419d0e149653d8ab709e/Screenshot-2024-12-28-at-6.08.09PM.png" data-mid="223882273" border="0" src="https://freight.cargo.site/w/1000/i/405373bbb4dcb73d427bc1c78768d37113585267640a419d0e149653d8ab709e/Screenshot-2024-12-28-at-6.08.09PM.png" />Team This work was greatly supported and completed thanks to the help of Manan Bhatt, Kiara Quinn, Siyu Wang and Pierce Perkins. https://aao-brain.com/Neuropulse-microelectronics-for-brain-health-2023-1 Fri, 27 Dec 2024 08:31:19 +0000 Alessandro Ascani Orsini https://aao-brain.com/Neuropulse-microelectronics-for-brain-health-2023-1 Neuropulse - microelectronics for brain health Since Fall 2023 https://aao-brain.com/Description-4 Fri, 27 Dec 2024 08:31:36 +0000 Alessandro Ascani Orsini https://aao-brain.com/Description-4 This was my first project in Dr. Irazoqui’s lab which I started and finished in the fall of 2023. I am currently still using Neuropulse in Dr. Thakor’s lab to monitor and modulate arousal. The objective was to build a one board version of the lab’s flagship device “the Bionode”. The Bionode is a highly versatile, compact, implantable and wearable device to monitor biosignals in the body (ExG and respiration) and for stimulation. Overview The previous version of the Bionode 5.3 (renamed Neuropulse for brain health monitoring through the ear) is reported in Pederson’s work (Pederson, 2019). It consists of 4 high temporal resolution bipolar recording channels (up to 12.5 KHz each) and 1 stimulation channel. <img width="1968" height="620" width_o="1968" height_o="620" data-src="https://freight.cargo.site/t/original/i/5cdd1c9f99fccfbbdae0c41bb25264d997a12bc71bf4ebfd7573e547c8c80d17/Screenshot-2024-12-27-at-7.13.55PM.png" data-mid="223861167" border="0" src="https://freight.cargo.site/w/1000/i/5cdd1c9f99fccfbbdae0c41bb25264d997a12bc71bf4ebfd7573e547c8c80d17/Screenshot-2024-12-27-at-7.13.55PM.png" />RECORDING- The signal is passed through two stages of ultra low noise op amps. The first filtering the signal in the range needed (using a lowpass and a highpass filter changing based on what it is we want to record) and the second amplifying the signal. The signal then goes to a 12-bits digital to analog converter and finally, the data is streamed out. STIMULATION - The stimulation is performed through a current controlled Howland pump (HCP) via a fifth bipolar channel. It takes the input from the MCU, passing it through a digital to analog converter (DAC) and then to the current pump. It uses an impedance measurament circuit to ensure the stimulation output is as expected, and a switch to connect the DAC output to the HCP input.<img width="1968" height="556" width_o="1968" height_o="556" data-src="https://freight.cargo.site/t/original/i/afa432c9e68c9199899ddf820d951107b60d5fa24c7238cf7a6a282baa274907/Screenshot-2024-12-27-at-11.54.58PM.png" data-mid="223867315" border="0" src="https://freight.cargo.site/w/1000/i/afa432c9e68c9199899ddf820d951107b60d5fa24c7238cf7a6a282baa274907/Screenshot-2024-12-27-at-11.54.58PM.png" /> TELEMETRY - All Neuropulse devices require a nearby base station for communication, utilizing a dedicated 2.45 GHz frequency band. This setup enables simultaneous recording and control of multiple implants. The base station consists of a custom shield board connected to a Raspberry Pi, packaging the information from the implant. The Raspberry Pi generates its own Wi-Fi network, permitting control of the implants and data storage from any connected device. Citation:&nbsp;Pederson, Daniel J., et al. "The bionode: A closed-loop neuromodulation implant." ACM Transactions on Embedded Computing Systems (TECS) 18.1 (2019): 1-20. Final Board and benchmarking <img width="2156" height="1252" width_o="2156" height_o="1252" data-src="https://freight.cargo.site/t/original/i/716770d9d126a19c3f4de15f2010006fbea25f64b3e19b2493d5243b61fd157f/Screenshot-2024-12-28-at-12.02.38AM.png" data-mid="223867386" border="0" src="https://freight.cargo.site/w/1000/i/716770d9d126a19c3f4de15f2010006fbea25f64b3e19b2493d5243b61fd157f/Screenshot-2024-12-28-at-12.02.38AM.png" /> <img width="2156" height="1240" width_o="2156" height_o="1240" data-src="https://freight.cargo.site/t/original/i/da9c12e43296488b2136ea3b08b47b2c51ad9d61a6c6edc133a0cda9351cd99f/Screenshot-2024-12-28-at-12.02.20AM.png" data-mid="223867387" border="0" src="https://freight.cargo.site/w/1000/i/da9c12e43296488b2136ea3b08b47b2c51ad9d61a6c6edc133a0cda9351cd99f/Screenshot-2024-12-28-at-12.02.20AM.png" /> <img width="558" height="326" width_o="558" height_o="326" data-src="https://freight.cargo.site/t/original/i/a0e2de9c87839f60fe63d9735bbf064811b224e5b6067e8c12c191af00c42e0e/Picture1.jpg" data-mid="223867377" border="0" src="https://freight.cargo.site/w/558/i/a0e2de9c87839f60fe63d9735bbf064811b224e5b6067e8c12c191af00c42e0e/Picture1.jpg" /> <img width="3294" height="1481" width_o="3294" height_o="1481" data-src="https://freight.cargo.site/t/original/i/ab08eec90c38c721a98fdd24a681bfd81ff60b28de6411fc05acb2a7bbd03346/IMG_8612.jpeg" data-mid="223867461" border="0" src="https://freight.cargo.site/w/1000/i/ab08eec90c38c721a98fdd24a681bfd81ff60b28de6411fc05acb2a7bbd03346/IMG_8612.jpeg" /> <img width="3354" height="2058" width_o="3354" height_o="2058" data-src="https://freight.cargo.site/t/original/i/dd4fa14e1a9c856727665769ea8925720acda44651be95e25dbe5859e3632c5b/GoodStimulation5K.png" data-mid="223867545" border="0" src="https://freight.cargo.site/w/1000/i/dd4fa14e1a9c856727665769ea8925720acda44651be95e25dbe5859e3632c5b/GoodStimulation5K.png" /> The board was assembled by hand and tested step by step as it was built. To test the front end, a sine function at different frequencies was used. In this case, the picture of the program from the computer shows the device is able to pick up the sine wave at 100 Hz for an EEG channel. To test the stimulation, a resistor was applied across the channel and the voltage and current from the impedance measurament was recorded.&nbsp; As shown in the last figure, a bipolar current pulse is correctly being formed. Recordings and current work The device was tested using rats and it was able to efficiently pick up vital signs. Currently Neuropulse is set to record ECG and EEG in rodents to determine their arousal state and modulate it. This will have applications for neurocritical care, acute brain injury detection and recovery, emergency devices, anesthesia and neurological assessments. We will update the website once closer to publication date. Recording of vital signs&nbsp; through the channels on different areas of the rodent (yellow- ear and tail; green- chest and tail; pink - ear and mastoid). It is possible to observe EEG activity from the pink channel. TeamThis device work was done thanks to the great efforts, help and support of Georgia Lawlor (Board design and debugging) and Sadid Khan (debugging). The animal study and arousal monitoring is currently being done with the help of Mingfend Cao, Prachi Agarwal and Pierce Perkins. https://aao-brain.com/A-compact-magnonic-sensor-for-brain-biomagnetic-studies-2023 Fri, 27 Dec 2024 08:12:28 +0000 Alessandro Ascani Orsini https://aao-brain.com/A-compact-magnonic-sensor-for-brain-biomagnetic-studies-2023 A compact magnonic sensor for brain biomagnetic studies Fall 2023 https://aao-brain.com/Description-3 Fri, 27 Dec 2024 08:14:08 +0000 Alessandro Ascani Orsini https://aao-brain.com/Description-3 This was another project I worked on during my first semester in Dr. Irazoqui’s lab. The objective was to build a more compact, economical and more precise magnetic sensor than SQUID. This was foreseen to be used for high density MEG recordings from deeper regions of the brain. Working Principle and simulationsThe working principle behind the functioning of this device relies on the use of magnons or spin waves. These are high frequency perturbations in the magnetization of a ferromagnetic material that propagate down the material. They are generally formed using antennas emitting the perturbation in a high strength magnetic field (depending on the properties of the gyromagnetic ratio of the ferromagnetic material) and they can be measured also using antennas by looking at their induced electromagnetic force.<img width="376" height="364" width_o="376" height_o="364" data-src="https://freight.cargo.site/t/original/i/9c78ae200594838aff5b62d8d4377b928ccb81dc647000442fdf740192c65ff7/Spin.png" data-mid="223874687" border="0" src="https://freight.cargo.site/w/376/i/9c78ae200594838aff5b62d8d4377b928ccb81dc647000442fdf740192c65ff7/Spin.png" /> Magnetization Dynamics of a magnetic domain in a ferromagnetic material, based on the Landau Lifshitz Gilbert equation. Heff is the external magnetic field while M the magnetization of a point on the material. Based on the proof of concept from Balynsky (Balynsky, 2017), magnons can interfere as regular electromagnetic waves by using a cross junction. They can also be affected by small changes in external magnetic field, changing their phase and thus their interference. In very stable materials such as Yttrium Iron Garnet (YIG) where the spin waves propagate for centimeters, this can be used to measure very small magnetic fields with very low levels of noise, depending on the sensitivity of the antennas and the precision in measuring their phase. This can also be shown with a MATLAB simulation using the Landau Lifshitz Gilbert model. <img width="713" height="529" width_o="713" height_o="529" data-src="https://freight.cargo.site/t/original/i/b4b4c2a147fc936e3128a1e9b22bfdf376cc9f7f8be883747298d647495b6f91/dev.png" data-mid="223874692" border="0" data-scale="87" src="https://freight.cargo.site/w/713/i/b4b4c2a147fc936e3128a1e9b22bfdf376cc9f7f8be883747298d647495b6f91/dev.png" /> <img width="1120" height="840" width_o="1120" height_o="840" data-src="https://freight.cargo.site/t/original/i/5c35e8577053f5c5442cb9faee048f54b07c043c835183df5c19f6123ccb3ad5/inductiveVoltPlot.png" data-mid="223874693" border="0" src="https://freight.cargo.site/w/1000/i/5c35e8577053f5c5442cb9faee048f54b07c043c835183df5c19f6123ccb3ad5/inductiveVoltPlot.png" /> Simulated output of inducted voltage based on the phase difference between spin waves (red line) and the phase read (blue line) and experimental data (Balynsky,2017)<img width="2098" height="1853" width_o="2098" height_o="1853" data-src="https://freight.cargo.site/t/original/i/8cd81cf8f5885e0075e1a38828c57dde14650fb4a97b92a30670dbb7ff98740d/IMG_7976.jpeg" data-mid="223874783" border="0" data-scale="74" src="https://freight.cargo.site/w/1000/i/8cd81cf8f5885e0075e1a38828c57dde14650fb4a97b92a30670dbb7ff98740d/IMG_7976.jpeg" /> YIG sample that we bought for the device. 1 cm x 1 cm. Citation&nbsp; &nbsp; - Balynsky, M., et al. "A magnetometer based on a spin wave interferometer." Scientific Reports 7.1 (2017): 11539. &nbsp; &nbsp; - Walker, W.D. “Sub-Microdegree Phase Measurement Technique Using Lock-in Amplifiers.” IEEE International Frequency Control Symposium. IEEE, (2008). 825–828. Device circuitry and design Reading the change in phase after the cross junction is then key to measuring magnetic fields at very small changes. This not only can tell the intensity of the field, but in a cross junction provides the 2D vector of the field, element never obtained from current high precision devices. Given the importance of the phase, we decided to implement a lock-in amplifier and use a technique of phase amplification (Walker, 2008) that subtracts the original signal from the recorded one.&nbsp; <img width="691" height="1102" width_o="691" height_o="1102" data-src="https://freight.cargo.site/t/original/i/5f437934529c3c7292f351c8bc92b5cb849d1c8a8061037fb54a808979fcc829/dev-copy.png" data-mid="223874691" border="0" data-scale="89" src="https://freight.cargo.site/w/691/i/5f437934529c3c7292f351c8bc92b5cb849d1c8a8061037fb54a808979fcc829/dev-copy.png" /> Schematics of the magnetic sensor design. The RF generator produces a 5GHz signal (B) which is used to generate the spin waves in the cross junction. As the spin waves interfere at the center, they move to the receiver where they are read by two antennas. From the original signal (B) the reading (A) is subtracted forming D. We then pass D through a lock in amplifier to extract its coordinates amplified (nX and nY) from which the phase (phi) can be estimated. Experimental design <img width="2044" height="1654" width_o="2044" height_o="1654" data-src="https://freight.cargo.site/t/original/i/03015e0350bc23acf5d13a74cbd11b2129a421b3aef6273cbfe26c2a2b5210d2/concept-art-outside.png" data-mid="223874782" border="0" data-scale="81" src="https://freight.cargo.site/w/1000/i/03015e0350bc23acf5d13a74cbd11b2129a421b3aef6273cbfe26c2a2b5210d2/concept-art-outside.png" /> <img width="2044" height="1654" width_o="2044" height_o="1654" data-src="https://freight.cargo.site/t/original/i/ae4006753c668c3456f5726e9da74f067aabc9bd24d2b74a2d461625e36dc26a/concept-art-inside.png" data-mid="223874784" border="0" data-scale="79" src="https://freight.cargo.site/w/1000/i/ae4006753c668c3456f5726e9da74f067aabc9bd24d2b74a2d461625e36dc26a/concept-art-inside.png" /> LimitationsThe project despite exciting faced several limitations (mostly related to the precision of the electronics) that didn’t permit it’s advancement beyond theoretical design: High frequency precision - Finding compact IC that could emit synchronously and precisely 5 GHz signals revealed itself to be quite the challenge. Often these are reserved for RF however when multiple are required, even small drifting in one of the emitters can make the device not work properlyStrong magnetic fields - The gyromagnetic ratio of the device required for 5GHz frequency a field of about 1 T (higher frequencies require higher fields). This is challenging to achieve and permanent magnets might be needed. However, small fluctuations in environment temperature can affect the magnetic field, affecting the resonant frequency of the device and the recordings. https://aao-brain.com/A-pocket-flow-cytometer-2023-1 Thu, 26 Dec 2024 12:49:17 +0000 Alessandro Ascani Orsini https://aao-brain.com/A-pocket-flow-cytometer-2023-1 A flow cytometer in a pocket Fall 2023 https://aao-brain.com/Description-2 Thu, 26 Dec 2024 12:52:44 +0000 Alessandro Ascani Orsini https://aao-brain.com/Description-2 This was a class long project done as part of the class “Advanced Topics in microfabrication” that I took in Fall 2023 with Dr. Andreas Andreou and&nbsp;Dr. Jeff Wang.The objective was to build using common microfabrication techniques in a clean room, a working flow cytometer on a chip from a silicon wafer. Flow cytometers are useful tools for cell counting and cell properties characterization. Design The flow cytometer consists of three inlets and one outlet. The central inlet is designated for the sample, while the two side inlets introduce a carrier liquid. These carrier liquids are maintained under constant pressure, directing the sample liquid to the center and focusing it into a narrow stream as it flows through the cross-junction. Beyond the cross-junction, a set of electrodes is present, where an electric field is applied. These fields align and channel the cells and samples passing through, making them easier to detect. A laser is directed at the region near the focusing electrodes, activating fluorescent markers attached to the cells. This allows precise counting of cells and, in samples with multiple cell types, allows for targeted marking and measurement of each cell type’s concentration. <img width="388" height="334" width_o="388" height_o="334" data-src="https://freight.cargo.site/t/original/i/7127a6d427a15199f00bb7aa47c515844cfec046fed1a87eae2c84064c15eced/focusingElectrodes.png" data-mid="223838936" border="0" src="https://freight.cargo.site/w/388/i/7127a6d427a15199f00bb7aa47c515844cfec046fed1a87eae2c84064c15eced/focusingElectrodes.png" /> <img width="890" height="592" width_o="890" height_o="592" data-src="https://freight.cargo.site/t/original/i/d8fac0edd46d1881ab06c27b729af3338a8d7450bc677c4270e23e2b17990358/Schematics.png" data-mid="223838938" border="0" data-scale="73" src="https://freight.cargo.site/w/890/i/d8fac0edd46d1881ab06c27b729af3338a8d7450bc677c4270e23e2b17990358/Schematics.png" /> Traditionally flow cytometers can be bulky and costly however, using microfabrication, it is possible to mass produce these microfluidic flow cytometers reliably in a relatively cost-effectly way. Process workflow <img width="1417" height="333" width_o="1417" height_o="333" data-src="https://freight.cargo.site/t/original/i/9fdcf378bb2712b7d039389f936d1c5e910f094f3286704d68e4675832b4b065/Flowchart.png" data-mid="223839814" border="0" data-scale="100" src="https://freight.cargo.site/w/1000/i/9fdcf378bb2712b7d039389f936d1c5e910f094f3286704d68e4675832b4b065/Flowchart.png" /> Final DeviceBelow are the final results from the flow cytometer, fabricated using both PDMS and anodic bonding techniques. Both devices function effectively, as demonstrated by cross-junction testing on the cover (the result shown was the anodic bonding one). Using a surface profilometer, the channel morphology of the device was analyzed. Irregularities observed at the channel bottom are attributed to the rapid etching reaction between KOH and silicon. However, for the purposes of this project, high precision is not important, making these imperfections negligible. <img width="570" height="428" width_o="570" height_o="428" data-src="https://freight.cargo.site/t/original/i/4734a1627fc26af8977de06b64e1d82e743a20035874e6326eca5643af36dd2c/FinalProduct.png" data-mid="223838935" border="0" src="https://freight.cargo.site/w/570/i/4734a1627fc26af8977de06b64e1d82e743a20035874e6326eca5643af36dd2c/FinalProduct.png" /> <img width="644" height="506" width_o="644" height_o="506" data-src="https://freight.cargo.site/t/original/i/1cd171e44b67164f89adf96f6621dcedbde75d5d20e9727cb519479825160f05/3Dscan.png" data-mid="223838937" border="0" src="https://freight.cargo.site/w/644/i/1cd171e44b67164f89adf96f6621dcedbde75d5d20e9727cb519479825160f05/3Dscan.png" /> If you are interested in a detailed report on this work, you can find it here. Team membersThe project was made with&nbsp;Max Kerensky and Ruixing Liang.