Research Highlights
Real time chemical and mechanical human motion monitoring with aerogel based wearable sensors
Real time chemical and mechanical human motion monitoring with aerogel based wearable sensors
Real time chemical and mechanical human motion monitoring with aerogel based wearable sensors
Real-time information from subtle human motion, such as heartbeat, etc., while concurrently monitoring sweat pH ion concentration, perspiration rate, etc.
Ergen, O., Celik, E., Unal, A. H., Erdolu, M. Y., Sarac, F. E., & Unal, U. (2020). Real time chemical and mechanical human motion monitoring with aerogel-based wearable sensors. Lab on a Chip, 20(15), 2689-2695.
Ultra-fast charging and safe battery geometries
Real time chemical and mechanical human motion monitoring with aerogel based wearable sensors
Real time chemical and mechanical human motion monitoring with aerogel based wearable sensors
Developing the 2D and 3D aerogel based separator, interface layer, and additives to solve the thermal problems of batteries, thus, allowing safe (no explosion or catching fire, operating range >200°C) and ultra-fast charging (extends the battery life at 10C, EV charge within 5min) while enhancing the battery capacity and power density.
Ergen, O. (2020). Hexagonal boron nitride incorporation to achieve high performance Li4Ti5O12 electrodes. AIP Advances, 10(4), 045040.
Ergen, O., & Zettl, A. K. (2020). High temperature Li-ion battery cells utilizing boron nitride aerogels and boron nitride nanotubes (No. 10,686,227). Lawrence Berkeley National Lab.(LBNL), Berkeley, CA (United States).
Ergen, O. (2017). Application of two dimensional and high surface area materials in energy conversion and storage devices (Doctoral dissertation, UC Berkeley).
Next-generation solar cell architectures
Real time chemical and mechanical human motion monitoring with aerogel based wearable sensors
Next-generation solar cell architectures
Screen Engineered Field Effect Solar Cells
Paving the way in future practical solar cell designs for hard to dope materials.
Ergen, O., Celik, E., Unal, A. H., & Erdolu, M. Y. (2020). Screen Engineered Field Effect Cu₂O Based Solar Cells. IEEE Electron Device Letters, 41(7), 1138-1140.
lithium-air battery architectures
Saliva detection and oral cancer
Next-generation solar cell architectures
These unique aerogel matrices exhibit the ability to orient the O2 passing through and keep out H2O, CO2, and N2. Thus, the solid-state cells demonstrate a long cycle life, thermal stability, and high rechargeable characteristics.
Ergen, O. (2017). Application of two dimensional and high surface area materials in energy conversion and storage devices (Doctoral dissertation, UC Berkeley).
Saliva detection and oral cancer
Saliva detection and oral cancer
Saliva detection and oral cancer
-This sensor array will be constructed with novel aerogel based microfluidics sensors and monitor multi-constituent saliva specimens, such as ions, oxidative, etc., while recoding saliva concentration, viscosity, and PH levels.
BOROPHENE
Saliva detection and oral cancer
Saliva detection and oral cancer
New fabrication method of borophene (2-Pmmn, b12, c3 and honeycomb phases, etc.) and its hybrid counterparts.
O. Ergen, “Method of growing large scale stable borophene using 2D layered and 3D aerogel templates for ultra-fast charging batteries”, 63169965, 2021
O.Ergen, et al., JACS, submitted
controlLING movement of each individual IoN
Piezotronics strain sensors for energy harvesting
controlLING movement of each individual IoN
Activating ballistic ion transport capability
O.Ergen, et al., PNAS, submitted
Food Fraud Identification
Piezotronics strain sensors for energy harvesting
controlLING movement of each individual IoN
Developing an effective and very simple method to verify high quality products with no additives, as well as organic food products, utilizing artificial intelligence. To our knowledge, this is the first report of an artificial intelligence-based tool utilizing simple sound vibrations to identify adulteration in food products.
Iymen, G., Tanriver, G., Hayirlioglu, Y. Z., & Ergen, O. (2020). Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration. Innovative Food Science & Emerging Technologies, 66, 102527.
Piezotronics strain sensors for energy harvesting
Piezotronics strain sensors for energy harvesting
Growth of III–V Semiconductors on 2D layered materials
Demonstrating flexible piezotronics strain sensor/nanogenerator, based on ZnO nanowires embedded on graphene aerogels.
O. Ergen, “Graphene Aerogel Based Nanogenerators for Health Monitoring. “, European Journal of Science and Technology, (21), 665-668, (2021).
O. Ergen, 2021, "ZnO Nanowire Embedded Graphene Aerogel Nanogenerators", (Online), International Congress of Natural Sciences
Growth of III–V Semiconductors on 2D layered materials
Computer vision and deep learning techniques in the oral cancer
Growth of III–V Semiconductors on 2D layered materials
New and alternative fabrication methods are necessary to restrict inherent lattice matching to favor widespread applications. Here, we demonstrated a new cost effective and simple method to synthesize III-V and III-VI semiconductors directly on top of layered materials. In this method, an active nucleation material is sandwiched between 2D layered materials, such as hexagonal boron nitride (h-BN), to produce a stacked structure. Thus, III-V or III-VI materials can grow and diffuse without afore- mentioned constraints.
O.Ergen, et al., AIP advance , submitted
Computer vision and deep learning techniques in the oral cancer
Computer vision and deep learning techniques in the oral cancer
Computer vision and deep learning techniques in the oral cancer
Exploring the potential applications of computer vision and deep learning techniques in the oral cancer domain within the scope of photographic images and investigated the prospects of an automated system for identifying potentially malignant oral disorders with a two-stage pipeline.
G. Tanriver, M.S. Tekkesin, O.Ergen, Cancers, in press
2D Dimensional and High Surface Area Materials in Water
Computer vision and deep learning techniques in the oral cancer
Computer vision and deep learning techniques in the oral cancer
Multi-functional tunable forward osmosis membranes, prepared with a unique geometry by embedding graphene aerogels and boron nitride aerogels as concentric circles.
O.Ergen, et al., Materials, submitted
To be submitted
Highly Stable Li Metal Anode Utilizing Boron Nitride Aerogel Based Coating
Creating Chemically and Mechanically Stable Super Ionic Conductors for Rechargeable Solid-state Lith
Creating Chemically and Mechanically Stable Super Ionic Conductors for Rechargeable Solid-state Lith
Creating Chemically and Mechanically Stable Super Ionic Conductors for Rechargeable Solid-state Lith
Creating Chemically and Mechanically Stable Super Ionic Conductors for Rechargeable Solid-state Lith
Creating Chemically and Mechanically Stable Super Ionic Conductors for Rechargeable Solid-state Lith
BACKSCATTERİNG
Creating Chemically and Mechanically Stable Super Ionic Conductors for Rechargeable Solid-state Lith
VİTAL SİGNS FROM FİNGER WRİNKLİNG
VİTAL SİGNS FROM FİNGER WRİNKLİNG
SMART STETHOSCOPE, (<2$), CAN MAKE A DİAGNOSİS THROUGH AI ALGORİTHMS
VİTAL SİGNS FROM FİNGER WRİNKLİNG
SMART STETHOSCOPE, (<2$), CAN MAKE A DİAGNOSİS THROUGH AI ALGORİTHMS
SMART STETHOSCOPE, (<2$), CAN MAKE A DİAGNOSİS THROUGH AI ALGORİTHMS
SMART STETHOSCOPE, (<2$), CAN MAKE A DİAGNOSİS THROUGH AI ALGORİTHMS
Proceedings
In Operando Noninvasive Lithium-Ion Battery Health Diagnoses Utilizing Sound Waves
In Operando Noninvasive Lithium-Ion Battery Health Diagnoses Utilizing Sound Waves
In Operando Noninvasive Lithium-Ion Battery Health Diagnoses Utilizing Sound Waves
Lithium-ion batteries (LIBs) are considered the key energy storage technology of the 21st century and have revolutionized the portable electronics and e-mobility segments. However, degradation mechanisms of LIBs, including lithium plating, conductivity, and active material loss, are very challenging to monitor for the Battery Management Systems (BMSs). Even though various non-invasive battery health diagnosis techniques are available, including impedance spectroscopy, pseudo-open circuit voltage, differential thermal voltammetry, incremental capacity differential voltage, etc., these methods have difficulty detecting early and sudden battery failures and determining the true state of health (SOH) at a given instant. For this reason, there is still a continued need for other non-invasive, cheap, and reliable monitoring methods that can provide real time SOH and degradation information to the BMSs. In this purpose, we developed a sound vibration-based sensing technique for monitoring the commercial lithium-ion battery’s SOH. The pulse vibrations are directly applied to positive and negative terminals and analyzed by artificial intelligence to identify degradation patterns. Thus, full operando experiments are able to be conducted to determine new battery health indicators for the BMSs. This proof-of-concept study outlines pulse-based sound vibrations and is a very effective method to achieve accurate degradation assessments along with early failure indications in LIBs.
O. Ergen, 2021, 7th International Congress On Engineering, Architecture And Design
Cos Effective Grid Edge Management Utilizing Wireless Information
In Operando Noninvasive Lithium-Ion Battery Health Diagnoses Utilizing Sound Waves
In Operando Noninvasive Lithium-Ion Battery Health Diagnoses Utilizing Sound Waves
The world’s energy demand is increasing rapidly due to population and economic growth, especially in emerging market economies. We need more energy resources to sustain growth of our industrialized world, but first we need to make drastic changes to our current global energy landscape by breaking away from our traditional reliance on fossil-fuel resources. At this point, renewable energy sources are an attractive alternative to fossil fuels, however their intermittency greatly complicates their integration into the current energy grid. Integration of these intermittent energy sources into the traditional grid system is extremely challenging and costly. For this reason, tracking the electric consumption in real time by developing a cloud based smart grid management platform plays an important role. However, the current data acquisition for this platform strongly relies on smart meters and smart appliance installations which are also very expensive, time-consuming, and creates latency issues in the cloud. Most importantly, the smart gadget installation concept is far from being universal and not applicable in many developing countries. Therefore, to dynamically construct a smart grid with an effective cloud-based management system, alternative and sustainable data collection techniques are required, which can overcome the limitations and promote universal solutions towards an efficient energy transition. The proposed research is embarking upon a detailed design to develop grid edge monitoring modeling tools that use existing infrastructure, gadgets, and materials to enable industry and electricity ecosystems. A real-time, easily implementable, and accurate electric consumption monitor is created by using available wireless information such as radio frequency measurements, received signal strength indicator (RSSI), Wi-Fi connection patterns, etc.
O. Ergen, 2021, International Congress on Engineering Sciences and Multidisciplinary Approaches
ZnO Nanowire Embedded Graphene Aerogel Nanogenerators
In Operando Noninvasive Lithium-Ion Battery Health Diagnoses Utilizing Sound Waves
ZnO Nanowire Embedded Graphene Aerogel Nanogenerators
Artificial intelligence (AI) and machine learning (ML) lead a new era in remote health monitoring and preventive care, while making ZnO based strain sensor and nanogenerators a very attractive data collection tool. Here, we demonstrate flexible piezotronics strain sensor/nanogenerator, based on ZnO nanowires embedded on graphene aerogels. The I-V characteristic of the sensor shows high sensitivity due to desirable piezotronics properties, piezopotential modulated changes in Schottky barrier height, under both static and dynamic loads. A good gauge factor of as high as 120 has been demonstrated, which is almost 50% higher than the gauge factor reported for any ZnO/Carbon based strain sensors.
O. Ergen, 2021, International Congress of Natural Sciences