RESEARCH

Ergen Quantum Applications Lab (EQAL): Nanoelectronics, Materials & Devices

Research Highlights

high-performance next-generation batteries & borophene aerogels

Digitize the Human Body by Backscattering Based Nano-Tattoos: Battery-Free Sensing

Controllable synthesis of borophene aerogels by utilizing h-BN layers for high-performance next-generation batteries

Borophene is emerging as a promising electrode material for Li, Na, Mg, and Ca ion batteries due to its anisotropic Dirac properties, high charge capacity, and low energy barrier for ion diffusion. However, practical synthesis of active and stable borophene remains challenging in producing electrochemical devices. Here, we introduce a method for borophene aerogels (BoAs), utilizing hexagonal boron nitride aerogels. Borophene grows between h-BN layers utilizing boron–boron bridges, as a nucleation site, where borophene forms monolayers mixed with sp2-sp3 hybridization. This versatile method produces stable BoAs and is compatible with various battery chemistries. With these BoAs, we accomplish an important milestone to successfully fabricate high-performance next-generation batteries, including Na-ion (478 mAh g–1, at 0.5C, >300 cycles), Mg-ion (297 mAh g–1, at 0.5C, >300 cycles), and Ca-ion (332 mAh g–1, at 0.5C, >400 cycles), and Li-S batteries, with one of the highest capacities to date (1,559 mAh g–1, at 0.3C, >1,000 cycles).

O. Ergen, “Method of growing large scale stable borophene using 2D layered and 3D aerogel templates for ultra-fast charging batteries”, 63169965, 2021

Çiftçi, N. O., Şentürk, S. B., Sezen, Y., Kaykusuz, S. Ü., Long, H., & Ergen, O. (2023). Controllable synthesis of borophene aerogels by utilizing h-BN layers for high-performance next-generation batteries. Proceedings of the National Academy of Sciences, 120(42), e2307537120.

https://doi.org/10.1073/pnas.230753712

Digitize the Human Body by Backscattering Based Nano-Tattoos: Battery-Free Sensing

Artificial intelligence (AI) lead a new era in remote health monitoring and preventive care, by making wearable electronics, specifically electronic skin, monumental in the future of healthcare. However, remote data collection from these sensors still largely relies on external circuits such as amplifiers, analog to digital converters, and batteries. This extra layer of circuity results in bulky systems, which greatly limits the application of these sensors and forces them to only work at certain places on the human body for limited time, such as only wrist. Here, we present an original technique for producing ink-based nano-tattoos that can be applied anywhere on the human body in various shapes and sizes and transmit data without requiring any external circuit elements, including batteries, by utilizing backscattering communication principles. Ink of nano-tattoos based on ZnO nanowires embedded in graphene aerogels can analyze and track quality of human body movements anywhere there is an ambient wireless signal and a smart phone. This method turns every surface into a potential sensor, digitizing the human body.

Belcastro, K. D., & Ergen, O. (2023). Digitize the Human Body by Backscattering Based Nano-Tattoos: Battery-Free Sensing. IEEE Electron Device Letters, 44(5), 849-852.

Ultra-fast charging and safe battery geomeTRIEs

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).

Simple air blow to charge Li-air, rechargeable, solid-state batteries using nano-engineered aerogel

Lithium air (Li-air) batteries have recently sparked significant research interest in battery technologies due to their high theoretical energy densities, 5–10 times higher than commercial Li-ion batteries. These cells, however, have several major flaws, such as electrode disintegration, a short cycle life, polarization losses, and air sensitivity, which prevents them from dominating the battery field. The most important challenge is to develop a high-throughput air-breathing cathode capable of delivering effective oxygen while excluding other contaminants (e.g., CO2 and H2O), so that ambient air can be used for intake. Here, we developed nano-engineered metal oxide-graphene aerogel matrix composites (NMOGAM), as active air breathing cathodes, specifically embedded with nanowires (Fe2O3) along with nanoparticles (TiO2, dolomite CaMg(CO3)2)), to enhance CO2 capture along with inner hydrophobic groups that remove water. As a result, rechargeable high-performance Li-air batteries with high electrochemical performances (up to 500 cycles in air with a capacity of 5850 mAh g−1) are developed, which can be charged with a simple blow/puff of air.

Ergen, O., Çiftci, N. O., & İbiş, Ö. (2022). Simple air blow to charge Li-air, rechargeable, solid-state batteries using nano-engineered aerogel structures. Electrochemistry Communications, 142, 107379.

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.

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.

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.

Food Fraud Identification

Artificial intelligence-based identification of butter variations as a model study for detecting food adulteration

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.

Instant Olive Oil Quality Detection and Rapid Food Evaluation: A User-Friendly and Attractive Artificial Intelligence Solution

The global demand for high-quality olive oil has witnessed unprecedented growth, driven by heightened health consciousness and consumer preferences for healthy dietary choices. However, this surging demand has made olive oil one of the most adulterated foods worldwide. Olive oil food adulteration can be done through chemical and thermal processing which causes reduced nutritional value as well as serious health risks for consumers. This emphasizes the critical importance of building an effective food fraud detection system for olive oil. In this study, we developed a new method that can instantly determine both adulteration and quality from a single smartphone image, offering a swift and user-friendly solution. A single droplet of oil dispersed across the water’s surface provides rapid and exact detection of food adulteration in seconds. Our method employs two specialized machine learning models: an unsupervised K-means clustering model and a supervised Convolutional Neural Network (CNN) for image analysis of olive oil on the water surface, a phenomenon affected by interfacial tension dynamics. These models excel in classifying olive oil quality, with a 99% accuracy rate, marking a tremendous leap in the identification of food fraud using Artificial Intelligence (AI).

M. A. Sarsıl, D. Dede, A. Seçilmis, G. Catalkaya, E. Capanoglu

A. Shaker, O. Ergen, Journal of Food Composition and Analysis, 2023, in press.

Piezotronics strain sensors for energy harvesting

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

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., Materials , submitted

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.

Tanriver, G., Soluk Tekkesin, M., & Ergen, O. (2021). Automated detection and classification of oral lesions using deep learning to detect oral potentially malignant disorders. Cancers, 13(11), 2766.

2D Dimensional and High Surface Area Materials in Water

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., Micromachines, submitted

Under-Review

PyNanospacing: TEM image processing tool for strain analysis and visualization

Abstract: The diverse spectrum of material characteristics including band gap, mechanical moduli, color, phonon and electronic density of states, along with catalytic and surface properties are intricately intertwined with the atomic structure and the corresponding interatomic bond-lengths. This interconnection extends to the manifestation of interplanar spacings within a crystalline lattice. Analysis of these interplanar spacings and the comprehension of any deviations, whether it be lattice compression or expansion, commonly referred to as strain, hold paramount significance in unraveling various unknowns within the field. Transmission Electron Microscopy (TEM) is widely used to capture atomic-scale ordering, facilitating direct investigation of interplanar spacings. However, creating critical contour maps for visualizing and interpreting lattice stresses in TEM images remains a challenging task. Here we developed a Python code for TEM image processing that can handle a wide range of materials including nanoparticles, 2D materials, pure crystals and solid solutions. This algorithm converts local differences in interplanar spacings into contour maps allowing for a visual representation of lattice expansion and compression. The tool is very generic and can significantly aid in analyzing material properties using TEM images, allowing for a more in-depth exploration of the underlying science behind strain engineering via strain contour maps at the atomic level.

Sarsil, M. A., Mansoor, M., Saracoglu, M., Timur, S., Urgen, M., & Ergen, O. (2023). PyNanospacing: TEM image processing tool for strain analysis and visualization. arXiv Preprint Archive, arXiv:2311.15751.

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Machine Learning Based Voice Analysis to Predict Mortality Rate among Patients with Heart Failure: An Interdisciplinary Approach

ABSTRACT: Heart failure (HF) is a prevalent and costly global health issue, demanding innovative approaches for better patient care. This study explores the intersection of voice analysis and machine learning as a non-invasive and accessible method for predicting mortality rates in hospitalized HF patients. This approach holds the potential to transform patient outcomes, resource allocation, and patient-cantered HF management by integrating vocal biomarkers into routine patient monitoring. In the study, a logistic regression model, a machine learning algorithm, is trained with the patients’ voice as input to predict the 5-year mortality rates of those patients. The model showcases superb and consistent performance which is validated with cross-validation and statistics techniques (p-value <0.001). Moreover, the inclusion of one of diagnostic biomarkers in HF, NT-proBNP, enhances predictive accuracy of the model noticeably.

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Programmable Quantum Electrochemical Battery Devices: Programmable Carriers Leveraging 2D Borophene/h-BN/Graphene Geometries

ABSTRACT: Classic electrochemical principles have been the foundation of battery research from the beginning, but there are still little to no control regarding carrier transport dynamics, along with many unanswered questions and uncertainties. Quantum electrochemical battery with programmable electron and ion is a fundamentally new concept that carrier properties can be programmed to achieve specific quantum transport behavior upon command. But it is very difficult to realize control over this carrier transport due to environmental noise and disorder in quantum particle and wave transport in solid state complex media. Here, we originally developed 2D borophene/h-BN/graphene-based device geometries as a controlled environment to study carrier dynamics. This system utilizing important quantum phenomenon’s such as coulomb blockage and ballistic transport in such a way that distinct transport regimes emerge that electron and ion movements can be controlled with distinctive background electrical pulses generated by artificial intelligence (AI) algorithms with accuracy of 87%. With this control, we successfully developed proof-of-concept quantum electrochemical battery configuration that can be initiated by Coulomb explosion that can charge instantly (>100ps) and capable of hold charge indefinitely with little waste.

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Voice-Based Blood Glucose Level Prediction: A Non-invasive Approach Using Artificial Intelligence

ABSTRACT: This project explores the feasibility of predicting blood glucose levels noninvasively by analysing voice samples through artificial intelligence (AI) techniques. Traditional methods of monitoring blood glucose involve invasive procedures, contributing to patient discomfort and the need for constant intervention. Leveraging the power of AI, our research aims to establish a correlation between the unique acoustic patterns in individuals' voices and their blood glucose levels. We will employ cutting-edge machine learning and signal processing algorithms to analyse e datasets, meaningful features (mel-cepstrum and etc) that reflect physiological changes associated with varying glucose concentrations. If successful, this non-invasive approach could revolutionize diabetes management, offering a convenient and continuous monitoring solution, thereby enhancing the quality of life for individuals with diabetes.

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Maximizing H2 Storage Efficiency through Boron Nitride Aerogel Fibers utilizing AI-assisted electrical pulses: Programmable activation surfaces

ABSTRACT: The potential of hydrogen as a sustainable energy source has garnered interest in recent years due to concerns about climate change and dwindling fossil fuel reserves. One major challenge in implementing hydrogen as an energy source is effective storage, and Boron Nitride Aerogel (BNA) has emerged as a promising material for hydrogen storage due to its high surface area, low density, and thermal stability. Here, we introduce an original method to very effectively store hydrogen by utilizing defect manipulated boron nitride aerogel fibers (BNAFs). BNAFs with various densities—as low as 12.5 mg/cc—and the highest specific surface area of up to 1162 m2 g-1 as grown and 1481.55 m2 g-1 with electrical pulse modulation, were created using a simple fabrication and functionalization method and enhanced up to 37.5% (1700.3 m2 g-1) by precents of electrical pulses by altering surface reconstruction. These materials exhibit one of the greatest reversible H2 uptake rates, with 6.2 wt% at ambient temperature at 10 bar of pressure, 5.4 wt % at 77 K and 1 bar, and 9.7 wt% at 77 K at 30 bar of pressure, when guided by artificial intelligence-based pulses. BNAFs have the potential to be the perfect hydrogen storage material for usage in practical applications because of their low weight, high surface area, and thermal stability.

O.Ergen “Maximizing H2 Storage Efficiency through Boron Nitride Aerogel Fibers utilizing AI-assisted electrical pulses: Programmable activation surfaces”, U.S Patent Application, 63/602,633, 2023

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Multielement Aerogels: High Entropy Compositions of Boron, Carbon, Nitrogen, and Associated Compound Aerogels

Abstract—The present invention introduces a novel approach to high-entropy alloy (HEA) formation, challenging the conventional understanding by demonstrating that high-entropy characteristics can be achieved not only with metallic elements but also with non-metallic elements, specifically boron, carbon, and nitrogen. This invention details the fabrication of high entropy aerogels (HEAg) and related terminal compounds (GrAg, BNAg) through configurational entropy considerations and molar ratio control during the pyrolysis of precursors containing these non-metallic elements.

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N. O. Ciftci, O. Ergen “Multielement Aerogels: High Entropy Compositions of Boron, Carbon, Nitrogen, and Associated Compound Aerogels” U.S Patent Application, 63/606,053, 2023

Creating Chemically and Mechanically Stable high Ionic Conductors for Rechargeable All Solid-state Lithium Ion and Lithium Sulfur Batteries Utilizing Boron Nitride Aerogels

ABSTRACT: Batteries are vital for a sustainable and low-carbon future; however, current battery technologies cannot keep up with increasing power, safety, and, especially, fast charging demands. A major battery revolution will soon be required and the next breakthrough in battery technology considered to be developing all solid state batteries due to their extraordinary safety and high power features. However, solid state batteries strongly suffer from low ionic conductivity and energy density due to lack of effective electrolyte structures. Here, we demonstrate an effective way to develop high ionic conductive solid state electrolytes utilizing functionalized boron nitride aerogel (BNAG) composite materials. NASICON, Garnet, and Sulfide type solid state electrolytes are studied and high performing batteries with high Li-ion conductivity, ion transport properties, and energy densities are developed.

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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

BACKSCATTERİNG

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

Proceedings

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

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

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