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Konrad Szaciłowski

Publications and source records attributed to Konrad Szaciłowski.

16 recordsLinked to original sources
arXiv2025

7-Methylquinolinium Iodobismuthate Memristor: Exploring Plasticity and Memristive Properties for Digit Classification in Physical Reservoir Computing

This study investigates 7-methylquinolinium halobismuthates (I, Br, and Cl) in two aspects: (1) their structural and semiconducting properties influenced by anionic composition, and (2) their memristive and plasticity characteristics for neuromorphic and reservoir computing applications. Structural changes induced by halides form low-dimensional halobismuthate fragments, confirmed by crystallographic analysis. Optical band gaps were studied using diffuse reflectance spectroscopy, aligning with density functional theory results. Due to solubility limitations, only bismuth iodide complexes were explored in electronic devices. Current-voltage scans showed pinched hysteresis loops, characteristic of memristors. Conductivity versus temperature study indicates combined ionic and electronic contributions to conductivity of the devices. Given that a memristor can function as a single synapse without the need for programming, aligning with the requirements of neuromorphic computing, the study investigated long-term depression, potentiation, and spike-time-dependent plasticity. As the potentiation-depression plots showed non-linearity with fading memory, these materials can be a good candidate for application in physical reservoir computing. To further assess this material, an electronic device with sixteen gold electrodes was applied, featuring one input and 15 output electrodes deposited on silicon substrate and covered with a layer of studied compound. Basic test to assess the complexity and non-linearity of the devices were conducted through a series of benchmark tasks, including waveform generation, NARMA-2, memory capacity assessment, and noise study under both DC and AC current. The ability of device in MNIST digit classification with 82.26% accuracy and voice classification for digit 2 for six different people with 82 % accuracy has been demonstrated.

cond-mat.dis-nn↗
arXiv2025

Memristive properties and synaptic plasticity in substituted pyridinium iodobismuthates

This study explores the impact of organic cations in bismuth iodide complexes on their memristive behavior in metal-insulator-metal (MIM) type thin-layer devices. The presence of electron-donating and electron withdrawing functional groups (-CN, -CH3, -NH2, and -N(CH3)2) on pyridinium cations induces morphological alterations in crystals, thus influencing the electronic or ionic conductivity of devices comprising sandwiched thin layers (thickness = 200 nm +/-50) between glass/ITO as bottom electrode (~ 110 nm) and copper (~ 80 nm) as the top electrode. It was found that the current-voltage (I-V) scans of the devices reveal characteristic pinched hysteresis loops, a distinct signature of memristors. The working voltage windows are significantly influenced by both the types of cation and the dimensionality of ionic fragments (0D or 1D) in the solid-state form. Additionally, the temperature alters the surface area of the I-V loops by affecting resistive switching mechanisms, corresponding log-log plots at three temperatures (-30 °C, room temperature and 150 °C) are fully studied. Given that a memristor can operate as a single synapse without the need for programming, aligning with the requirements of neuromorphic computing, the study investigates long-term depression, potentiation, and spike-time-dependent plasticity-a specific form of the Hebbian learning rule-to mimic biologically synaptic plasticity. Different polar pulses, such as triangle, sawtooth, and square waveforms were employed to generate Hebbian learning rules. The research demonstrates how the shape of the applied pulse series, achieved by overlapping pre- and post-pulses at different time scales, in association with the composition and dimensionality of ionic fragments, lead to changes in the synaptic weight percentages of the devices.

cond-mat.mtrl-sci↗
arXiv2025

Influence of Crystal Structure and Composition on Optical and Electronic Properties of Pyridinium-based Bismuth Iodide Complexes

This study investigates the impacts of structure and composition on the optical and electronic properties of a series of pyridinium-based bismuth iodide complexes. Organic substrates with various functional groups, such as 4-aminopyridine (4-Ampy), 4-methylpyridine (4-Mepy), 4-dimethyaminopyridine (4-Dmapy), and 4-pyridinecarbonitrile (4-CNpy) with different electron-donating and electron-withdrawing groups at the para position of the pyridine ring were employed. Crystallographic analysis reveals various bismuth iodide structures, including 1D chains and discrete 0D motifs. The optical band gap of these materials, identified via Diffuse reflectance spectroscopy (DRS) and verified with density functional theory (DFT) calculations, is influenced by the crystal packing and stabilising interactions. Through a comprehensive analysis, including Hirshfeld surface (HS) and void assessment, the study underscores the influence of noncovalent intermolecular interactions on crystal packing. Spectroscopic evaluations provide insights into electronic interactions, elucidating the role of electron donor and acceptor substituents within the lattice. Thermogravimetric differential thermal analysis (TG-DTA) indicates structural stability up to 250°C. Linear sweep voltammetry (LSV) reveals significant conductivity in the range of 10-20 mS/pixel at 298.15 K. X-ray absorption spectroscopy (XAS) at the Bi L3 edge indicates a similar oxidation state and electronic environment across all samples, underscoring the role of bismuth centres surrounded by iodides.

cond-mat.mtrl-sci↗
arXiv2025

Cyanothiazole Copper(I) Complexes: Uncharted Materials with Exceptional Optical and Conductive Properties

Cyanothiazoles, small and quite overlooked molecules, possess remarkable optical properties that can be fine-tuned through coordination with transition metals. In this study, we investigate a promising application of cyanothiazoles, where their combination with copper(I) iodide forms a new class of complexes exhibiting outstanding optical properties. X-ray crystallography of copper(I) iodide complexes with isomeric cyanothiazoles revealed key structural features, such as π-π stacking, hydrogen bonding, and rare halogen-chalcogen I-S interactions, enhancing stability and reactivity. Advanced spectroscopy and computational modeling allowed precise identification of spectral signatures in FTIR, NMR, and UV-Vis spectra. Fluorescence studies, along with XANES synchrotron analyses, highlighted their unique thermal and electronic properties, providing a solid foundation for further research in the field.

cond-mat.mtrl-sci↗
arXiv2025

Structural Insights and Advanced Spectroscopic Characterization of Thiazolothiazoles: Unveiling Potential for Optoelectronic and Sensing Applications

Thiazolothiazoles (TzTz) represent a class of compounds with distinctive structural motifs and exceptional optical properties, positioning them as promising candidates for breakthroughs in optoelectronic and sensing technologies. X-ray crystallographic analyses of TzTz units symmetrically substituted with functional groups such as imidazole, o-vanillin, p-vanillin, phenyl, thiazole, cinnamate, and bistrifluoromethylphenyl have revealed complex structural features, including π-π stacking interactions, hydrogen-bond networks, and specific chalcogen and halogen interactions. These interactions collectively enhance the stability and define the unique spectroscopic profiles of these compounds. Beyond classical spectral fingerprints (FTIR, NMR, and UV-Vis spectra), fluorescence studies at various temperatures, complemented by XANES synchrotron analyses, have underscored their remarkable thermal and electronic properties. The findings presented here offer a comprehensive framework for the characterization and analysis of TzTz compounds, emphasizing their potential as components in smart electronic and optoelectronic devices.

cond-mat.mtrl-sci↗
arXiv2025

Silver(I) complexes with nitrile ligands: new materials with versatile applications

In the present study, the structure, thermal stability, conductive properties, and antimicrobial activity of silver(I) complexes with nitrile ligands were investigated. For the construction of the materials, 2 cyanopyridine (2-cpy), 4-cyanopyridine (4-cpy), 1,2-dicyanobenzene (1,2-dcb), and 1,3 dicyanobenzene (1,3-dcb) were used in addition to the silver nitrite and nitrate. Four new compounds were isolated and structurally characterized: one molecular complex [Ag4(1,2-dcb)4(NO3)4], two 1-D coordination polymers [Ag2(2-cpy)2(NO2)2], [Ag3(1,3-dcb)2(NO3)2] and one 3-D coordination polymer [Ag(4-cpy)(NO2)]. The results indicate that the nitrite complexes display good antimicrobial properties against the tested bacterial and fungal strains. The presence of weakly coordinating CN groups increases the release of silver ions into the bacterial and yeast cell environments. Moreover, these materials exhibit unusual electrical properties in thin-layer devices. On the other hand, the nitrite and nitrate counterions give rise to the low thermal stability of the complexes.

cond-mat.soft↗
arXiv2025

Reservoir Computing and Photoelectrochemical Sensors: A Marriage of Convenience

Sensing technology is an important aspect of information processing. Current development in artificial intelligence systems (especially those aimed at medical and environmental applications) requires a lot of data on the chemical composition of biological fluids or environmental samples. These complex matrices require advanced sensing devices, and photoelectrochemical ones seem to have potential to overcome at least some of the obstacles. Furthermore, the development of artificial intelligence (AI) technology for autonomous robotics requires technology mimicking human senses, also those operating at the molecular level, such as gustation and olfaction. Again, photoelectrochemical sensing can provide some suitable solutions. In this review, we introduce the idea of integration of photoelectrochemical sensors with some unconventional computing paradigm - reservoir computing. This approach should not only boost the performance of the sensors itself, but also open new pathways through science. Integration of sensing devices with computing systems will also contribute to a better understanding (or at least mimicking) of the human senses and neuromorphic sensory information processing. Although reservoir systems can be considered magic "black boxes" and their operation is at the same time simple and hard to comprehend, this combination is expected to open a new era of effective information harvesting and processing systems.

physics.ins-det↗
arXiv2025

KNOWM Memristors in a Bridge Synapse delay-based Reservoir Computing system for detection of epileptic seizures

Nanodevices that show the potential for non-linear transformation of electrical signals and various forms of memory can be successfully used in new computational paradigms, such as neuromorphic or reservoir computing (RC). Dedicated hardware implementations based on functional neuromorphic structures significantly reduce energy consumption and/or increase computational capabilities of a given artificial neural network system. Concepts of RC, which as a flexible computational paradigm can be highly inclusive, are often used as a model to describe computations performed in materia. With mostly fixed internal structure, solid-state devices, especially memristors, are studied as computational substrates in various RC systems. In this work, we present single-node Echo State Machine (SNESM) RC system based on bridge synapse as a computational substrate (consisting of 4 memristors and a differential amplifier) used for epileptic seizure detection. KNOWM memristors were posed as ideal candidates because of their easy prototyping and reliability of operation. In this account, we present an application of commercially available KNOWM memristors in various neuromorphic applications, from simple analysis of switching and internal dynamics (elucidated form noise spectroscopy and total harmonic distortion analysis) to the classification and recognition of complex time series: epilepsy seizure recognition using a wrist-worn triaxial accelerometer.

physics.med-ph↗
arXiv2022

Evidence of In-Memory Computing in a Ferrofluid

Magnetic fluids are excellent candidates for important research fields including energy harvesting, biomedical applications, soft robotics and exploration. However, notwithstanding relevant advancements such as shape reconfigurability, that have been demonstrated, there is no evidence for their computation capability, including the emulation of synaptic functions. Here, we experimentally demonstrate that a Fe3O4 water-based Ferrofluid (FF) can perform electrical analog computing and be programmed using quasi DC signals and read at Radio Frequency (RF) mode. We have observed features in all respects attributable to a memristive behavior, featuring both short and long-term information storage capacity and plasticity. The colloid was capable of classifying digits of a 8x8 pixel dataset using a custom in-memory signal processing scheme, and through Physical Reservoir Computing (PRC) by training a readout layer.

cs.ET↗
arXiv2020

Information, communication and music: Recognition of musical dissonance and consonance in a simple reservoir computing system

Reservoir computing is an emerging, but very successful approach towards processing and classification of various signals. It can be described as a model of a transient computation, where influence of input changes internal dynamics of chosen computational reservoir. Trajectory of these changes represents computation performed by the system. The selection of a suitable computational substrate capable of non-linear response and rich internal dynamics ensures the implementation of simple readout protocols. Signal evolution based on the rich dynamics of the reservoir layer helps to emphasize differences between given signals thus enabling their easier classification. Here we present a simple reservoir computing system (single node echo-state machine) implemented on Multisim platform as a tool for classification of musical intervals according to their consonant or dissonant character. The result of this classification closely resembled sensory dissonance curve, with some significant differences. A deeper analysis of the received signals indicates the geometric relationships between the consonant and dissonant intervals, enabling their classification.

cs.SD↗
arXiv2020

Computing with bricks and mortar: Classification of waveforms with a doped concrete blocks

We present results showing the capability of concrete-based information processing substrate in the signal classification task in accordance with in materio computing paradigm. As the Reservoir Computing is a suitable model for describing embedded in materio computation, we propose that this type of presented basic construction unit can be used as a source for "reservoir of states" necessary for simple tuning of the readout layer. In that perspective, buildings constructed from computing concrete could function as a highly parallel information processor for smart architecture. We present an electrical characterization of the set of samples with different additive concentrations followed by a dynamical analysis of selected specimens showing fingerprints of memfractive properties. Moreover, on the basis of obtained parameters, classification of the signal waveform shapes can be performed in scenarios explicitly tuned for a given device terminal.

cs.ET↗
arXiv2020

In-materio neuromimetic devices: Dynamics, information processing and pattern recognition

The story of information processing is a story of great success. Todays' microprocessors are devices of unprecedented complexity and MOSFET transistors are considered as the most widely produced artifact in the history of mankind. The current miniaturization of electronic circuits is pushed almost to the physical limit and begins to suffer from various parasitic effects. These facts stimulate intense research on neuromimetic devices. This feature article is devoted to various in materio implementation of neuromimetic processes, including neuronal dynamics, synaptic plasticity, and higher-level signal and information processing, along with more sophisticated implementations, including signal processing, speech recognition and data security. Due to vast number of papers in the field, only a subjective selection of topics is presented in this review.

cs.ET↗
arXiv2020

Reservoir computing for sensing: an experimental approach

The increasing popularity of machine learning solutions puts increasing restrictions on this field if it is to penetrate more aspects of life. In particular, energy efficiency and speed of operation is crucial, inter alia in portable medical devices. The Reservoir Computing (RC) paradigm poses as a solution to these issues through foundation of its operation: the reservoir of states. Adequate separation of input information translated into the internal state of the reservoir, whose connections do not need to be trained, allow to simplify the readout layer thus significantly accelerating the operation of the system. In this brief review article, the theoretical basis of RC was first described, followed by a description of its individual variants, their development and state-of-the-art applications in chemical sensing and metrology: detection of impedance changes and ion sensing. Presented results indicate applicability of reservoir computing for sensing and validating the SWEET algorithm experimentally.

cs.ET↗
arXiv2019

Towards synthetic neural networks: Can artificial electrochemical neurons be coupled with artificial memristive synapses?

The enormous amount of data generated nowadays worldwide is increasingly triggering the search for unconventional and more efficient ways of processing and classifying information, eventually able to transcend the conventional von-Neumann-Turing computational central dogma. It is, therefore, greatly appealing to draw inspiration from less conventional but computationally more powerful systems such as the neural architecture of the human brain. This neuromorphic route has the potential to become one of the most influential and long-lasting paradigms in the field of unconventional computing. The material-based workhorse for current hardware platforms is largely based on standard CMOS technologies, intrinsically following the above mentioned von-Neumann-Turing prescription; we do know, however, that the brain hardware operates in a massively parallel way through a densely interconnected physical network of neurons. This requires challenging the intrinsic definition of the single units and the architecture of computing machines. (...)

nlin.AO↗
arXiv2019

Liquid metal solves maze

A room temperature liquid metal features a melting point around room temperature. We use liquid metal gallium due to its non-toxicity. A physical maze is a connected set of Euclidean domains separated by impassable walls. We demonstrate that a maze filled with sodium hydroxide solution is solved by a gallium droplet when direct current is applied between start and destination loci. During the maze solving the droplet stays compact due to its large surface tension, navigates along lines of the highest electrical current density due its high electrical conductivity, and goes around corners of the maze's corridors due to its high conformability. The droplet maze solver has a long life-time due to the negligible vapour tension of liquid gallium and its corrosion resistance and its operation enables computational schemes based on liquid state devices.

cs.ET↗