The realization of nanoscale devices largely depends on our ability to control and manipulate both the molecular interactions and charge transport within and among molecules. The field of molecular electronics uses molecules as electronic components and has intriguing applications in a variety of fields, including storage devices, logic circuits, optoelectronics, and biosensors. Molecular electronics offer various fundamental advantages. First, the size of molecules used is in the realm of nanometers, and thus the device packing densities can be increased with lower cost, high efficiency, and low power dissipation. Second, one can use specific intermolecular interactions to form desired geometries via self-assembly in a bottom-up fashion. Therefore, various organic and inorganic molecules have been the subject of extensive research to engineer novel electronic components. For more than two decades, DNA/RNA has been at the forefront of molecular electronics research. Facilitated by the advance of synthetic biology, designer DNA with predetermined sequences can be readily synthesized for various applications (e.g., biosensors, single molecule transistors, DNA origami, DNA nanopores and DNA storage) in the emerging field of molecular electronics. However, the lack of control of charge transport significantly limits its application in nanoelectronics. In this talk, we describe how novel peptides and/or oligonucleotides can be designed with specific charge transport properties using novel computational/engineering approaches and give examples of their potential utilizations in technology and medicine.

The design of sensor systems capable of functioning in ambient air or liquid media is highly sought after for label-free sensing in environmental and biological problems. Within this vision, I will outline various sensor platforms we have developed with micro/nanoscale resonators, using nano-electromechanical systems (NEMS) and microwave technologies. Nanomechanical resonators can achieve mass resolution on the order of several MegaDaltons (MDa) under ambient conditions, enabling the characterization of single nanoparticles and viruses that are in the hundred-MDa mass range. However, transporting analytes to the nanoscale sensing region had remained a significant hurdle. With the integration of an ion lens on the NEMS chip 1, we demonstrated high-efficiency nanoparticle detection using the self-focusing NEMS technology. We recently improved this technique with the use of devices featuring a central paddle-like collection area, which yields higher efficiency and requires simpler electronics 2. While NEMS devices are sensitive to inertial mass changes, microwave resonators can probe the same analytes through their electrical polarizability. For detection of cells and nanoparticles in liquid, we have developed microwave sensors with micro/nanoscale sensing regions. I will give an overview of recent experiments, such as classifying materials based on permittivity at the microscale 3, detecting nanoparticles using a microwave nanopore 4, and more recently conducting drug-resistance tests on cancer cells. I will conclude by discussing potential trends for novel sensing scenarios, such as multi-physical sensing.

Sabancı Üniversitesi yeni lisans programı Veri Bilimi ve Analitiği ve programın disiplinlerarası niteliklerini vurgulayan konferans dizisinin ikinci konuğu SAP Türkiyenin Genel Müdürü Uğur Candan olacak.

The recent boom and success of machine learning methods has encouraged efforts in synthesizing controllers that leverage neural networks as function approximators. However, when such controllers are synthesized it is important to take precautions against their potential vulnerabilities against disturbances arising from model uncertainties or measurement noise. In this work we address the automatic robust data-driven controller synthesis problem for robotic manipulation and locomotion. We demonstrate the efficacy of our theoretical results in simulation and real-world experiments on a rimless-wheel and a cart-pole system that contains walls. Our approach performs repeated interactions with a nominal dynamical model to infer a contact-aware passivity-based controller, whose storage function is given by a fully-connected neural network. Contacts, impacts and Coulomb friction are modeled through the linear complementarity problem (LCP), and solved via Lemkes algorithm, which allows us to take pertinent gradients for the data-driven technique. Additionally, we improve the robustness properties of the controller under model uncertainties, such as the rimless wheel traversing on uneven terrain, via Bayesian learning.

Josephson junctions are cornerstones of cryogenic classical and quantum superconducting technology, owing to their nonlinearity. Josephson tunnel junctions exhibit a simple current-phase relation characterized by single harmonics, whereas high-transparency Josephson junctions feature multiple harmonics depending on microscopic details of the junction, presenting a challenge for precise control. In this talk, I will illustrate that two Josephson tunnel junctions connected in series can mimic a high-transparency Josephson junction. By connecting multiple arms in parallel and adding a magnetic flux, we can systematically engineer specific current-phase relationships. As an example, I will showcase a high-efficiency superconducting diode, a two-terminal device that controls supercurrent directionally and remains robust against design imperfections, making it practical for real-world implementations. Additionally, I will present various engineered energy-phase relationships, showcasing the versatility of this technique in developing advanced quantum technologies.

Artificial intelligence is rapidly becoming a transformative tool in scientific discovery, playing a crucial role in accelerating research across various fields. In materials science, it has shown the potential to significantly augment traditional methods by providing insights that might have been unattainable using conventional approaches alone. However, the success of machine learning models in materials science is highly dependent on the availability and quality of data. Collecting high-quality datasets remains a major challenge, especially in materials science, where experimental data is often sparse and buried in the unstructured language of scientific literature. AI offers promising solutions for automating data extraction from these vast text resources. Beyond data extraction, it is also being applied to predict material properties, optimize experimental design, and accelerate the discovery of new materials. In this seminar, I will elaborate on how I am integrating AI to address the challenges of data mining from scientific literature. I will also discuss broader AI applications through specific examples, showcasing how AI is reshaping the landscape of materials science.

This talk looks at the feedback control of open-loop systems which have poles in the open right-half plane. Specifically, the inverted pendulum (cart-pole) system is considered. This system can be stabilized using feedback of just the cart position requiring an unstable controller to do so. However, the resulting (stable) closed loop system is too sensitive to be of any practical value. By sensitive is meant that a small reference input to the system will cause its output to vary far and wide before finally converging to the desired operating point. In fact, the output varies so far away that the linear model the controller design was based upon is no longer valid. This then results in a failed operation when applied to the actual inverted pendulum whose model is nonlinear. Further, the practical impossibility of using a feedback controller based on just the cart position is identified by the inverted pendulums right half-plane pole. Finally, it is shown why a linear state feedback controller does not have this sensitivity problem and does work to keep the pendulum rod upright. The generalization of Bodes theorem by Freudenberg & Looze is the key to understanding these claims.

Prior studies have described the complex interplay that exists between glioma cells and neurons however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain.

Classification of materials based on their physical properties has shaped the development of modern civilisations. Using the right materials together with fine-tuning of their physical properties, has enabled novel engineered devices. Over the last decade, we have witnessed a new materials classification based on topological concepts. We can group materials using integer numbers associated with the symmetry of their electronic band structure. Recently those innovative concepts have been applied for optical systems to investigate topological phases of light on tailored optical materials. However, examples in the literature are passive optical systems with built-in topology. Active control of topology and device applications remains a challenge. This talk explores the topological control of light across different domains, demonstrating how topological singularities can be harnessed to manipulate electromagnetic waves. First, I will show how exceptional points (EPs) singularities in graphene-based terahertz devices enable precise control over light propagation. By tuning a gate voltage, we can modulate both the intensity and phase of terahertz pulses using a topological phase transition while crossing an EP. We were able to reconstruct the complex energy landscape and explore non-Hermitian physics behind the observed topological properties. In a second approach, we apply topological principles to thermal light. By controlling the reflection topology of thermal emitters, we observed a topological phase transition in thermal radiation by varying a single parameter. The critical point of zero reflection is topologically protected, and boundaries between spatial domains host interface states with near-unity thermal emissivity. Together, these examples illustrate rich physics of topological singularities providing new tools for controlling light, from the terahertz regime to thermal radiation, with potential applications in THz communications and thermal management.

All interested are cordially invited to ICANN & Sabancı University FENS joint webinar on Technical communities make the Internet work and building trust. The webinar will be moderated by Albert Levi and Kürşat Çağıltay and will feature Herv