Events

Quasicrystal-reinforced aluminum metal matrix composites exhibit combinations of properties that are attractive for metal additive manufacturing. However, it is challenging to evaluate such systems due to difficulties in modeling the solidification and microstructural evolution. In this talk, I will introduce an emerging strategy to evaluate novel metal matrix composites for use in additive manufacturing. Laser glazing in a powder bed fusion instrument was used as an efficient method to evaluate an Al85Cu6Fe3Cr6 alloy, and the resulting microstructures were characterized using electron microscopy. Since the quasicrystal reinforcement phase is inherently metastable, it is important to understand the thermal decomposition (crystallization) of this phase, and in situ heating studies can provide unique insights. I will present findings on the thermal stability of these laser-processed microstructures, analyzed through in situ scanning transmission electron microscopy heating experiments. Additionally, the understanding the complex atomic arrangements in the structure of quasicrystals is challenging, we employ comprehensive methodology to analyze statistically generated ground states of orthorhombic approximants using density functional theory to shed light on the role of elemental variations of such complex alloys.

In real-life thermodynamic systems, energy loss is inevitable since they do not operate adiabatically. Therefore, minimizing energy loss is a crucial goal to improve the efficiency of a heat engine. In small scale heat engines, the amount of work done by the machine or the heat it absorbs from the heat source is also small due to the limited number of degrees of freedom. For this reason, fluctuations in thermodynamic quantities become significant at this small scale. The concept of thermodynamic geometry has emerged as a valuable tool for working on the optimization of a small heat engine. The thermodynamic geometry approach provides a suitable framework for exploring the relationships between quantities directly related to efficiency, such as energy loss and work. In micro-scale heat engines, fluctuations in energy loss are also expressed within the framework of thermodynamic geometry 1. To our knowledge, minimizing both energy loss and its fluctuations simultaneously remains an unsolved problem. In this talk, I will first briefly discuss the thermodynamic geometry formulation. I will show its connection to efficiency optimization for an example of a small heat engine and discuss whether we can simultaneously optimize energy loss and its fluctuations using this formalism.

Deep neural networks (DNNs) have shown great success in many applications such as image classification, natural language processing, and two-player games. DNNs could achieve good levels of precision, but they have a black box nature. As a result, the interpretation and the explanation of their decisions have become very crucial, especially when extending their application to critical fields.

We study a make-to-stock manufacturing system in which a single server makes the production. The server consumes energy, and its power consumption depends on the server state: a busy server consumes more power than an idle server, and an idle server consumes more power than a turned-off server. When a server is turned on, it completes a costly set-up process that lasts a while. We jointly control the finished goods inventory and the servers energy consumption. The objective is to minimize the long-run average inventory holding, backorder, and energy consumption costs by deciding when to produce, when to idle or turn off the server, and when to turn on a turned-off server. Because the exact analysis of the problem is challenging, we consider the asymptotic regime in which the server is in the conventional heavy-traffic regime. We formulate a Brownian control problem (BCP) with impulse and singular controls. In the BCP, the impulse control appears due to server shutdowns, and the singular control appears due to server idling. Depending on the system parameters, the optimal BCP solution is either a control-band or barrier policy. We propose a simple heuristic control policy from the optimal BCP solution that can easily be implemented in the original (non-asymptotic) system. Furthermore, we prove the asymptotic optimality of the proposed control policy in a Markovian setting. Finally, we show that our proposed policy performs close to optimal in numerical experiments.

The concept of time essentially says that we can know the future if we are given the state of a system right now. Hence, time is an essential part of our understanding of nature, and its existence is usually taken as granted in a mathematical formulation of a theory if we follow some simple guidelines. We will refute this idea in this talk, demonstrating that some of the simplest theories one can write down, such as self-interacting vector field theories, do not have indefinite time evolution. Namely, time evolution exists for a finite duration, but dynamics becomes impossible afterwards. We will discuss how this bizarre occurrence can be used to rule out many proposals in physics, helping us constrain the landscape of alternative theories in gravity and beyond. This is especially valuable in an era of abundance in alternative theoretical ideas and a relative scarcity of experiments and observations to constrain them.

Global warming may be one of the most important and the toughest challenges humanity has faced. Considering that our hydrocarbon combustion-centered electricity generation is the main source of carbonaceous gas emissions - the root cause of global warming - working towards a cleaner electricity production scheme appears to be critical to the accomplishment of this task. Adopting renewable energy technologies, such as solar and wind, seem quite straight-forward in this case. To render these technologies viable, their intermittency mitigated. Solid oxide cells (SOCs) are high-temperature (600-800 C) electrochemical devices that can both convert electricity and water to hydrogen and oxygen hydrogen and vice versa a trait that can address the intermittency problem of renewables. In our group we deal with the materials science-related problems associated with the SOC technology. We attempt to enhance the performance of the electrode and electrolyte materials through new chemistry development, design of novel synthesis and fabrication routes to obtain more favorable microstructures. Moreover, we conduct mechanistic studies to understand the mechanism through which performance degradations during long-term operation occur and attempt to mitigate them.

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.