Due to the relatively low efficiency of magnetic hyperthermia and photothermal conversion, it is rather challenging for magneto-photothermal nanoagents to be used as an effective treatment during tumor hyperthermal therapy. The advancement of magnetic nanoparticles exhibiting a vortex-domain structure holds great promise as a viable strategy to enhance the application performance of conventional magnetic nanoparticles while retaining their inherent biocompatibility. Here, we report the development of Mn0.5Zn0.5Fe2O4 nanoflowers with ellipsoidal magnetic cores, and show them as effective nanoagents for magneto-photothermal synergistic therapy. Comparative studies were conducted on the heating performance of anisometric Mn0.5Zn0.5Fe2O4 (MZF) nanoparticles, including nanocubes (MZF-C), hollow spheres (MZF-HS), nanoflowers consisting of ellipsoidal magnetic cores (MZF-NFE), and nanoflowers consisting of needle-like magnetic cores (MZF-NFN). MZF-NFE exhibits an intrinsic loss parameter (ILP) of up to 15.3 N h m2 kg-1, which is better than that of commercial equivalents. Micromagnetic simulations reveal the magnetization configurations and reversal characteristics of the various MZF shapes. Additionally, all nanostructures displayed a considerable photothermal conversion efficiency rate of more than 18%. Our results demonstrated that by combining the dual exposure of MHT and PTT for hyperthermia treatments induced by MZF-NFE, BT549, MCF-7, and 4T1 cell viability can be significantly decreased by â¼95.7% in vitro.
Exploring multiple states based on the domain wall (DW) position has garnered increased attention for in-memory computing applications, particularly focusing on the utilization of spin-orbit torque (SOT) to drive DW motion. However, devices relying on the DW position require efficient DW pinning. Here, we achieve granular magnetization switching by incorporating an HfOx insertion layer between the Co/Ti interface. This corresponds to a transition in the switching model from the DW motion to DW nucleation. Compared to the conventional Pt/Co/Ti structure, incorporation of the HfOx layer results in an enhanced SOT efficiency and a lower switching current density. We also realized stable multistate storage and synaptic plasticity by applying pulse current in the Pt/Co/HfOx/Ti device. The simulation of artificial neural networks (ANN) based on the device can perform digital recognition tasks with an accuracy rate of 91%. These results identify that DW nucleation with a Pt/Co/HfOx/Ti based device has potential applications in multistate storage and ANN.
The forefront of neuromorphic research strives to develop devices with specific properties, i.e., linear and symmetrical conductance changes under external stimuli. This is paramount for neural network accuracy when emulating a biological synapse. A parallel exploration of resistive memory as a replacement for conventional computing memory ensues. In search of a holistic solution, the proposed memristive device in this work is uniquely poised to address this elusive gap as a unified memory solution. Opposite biasing operations are leveraged to achieve stable abrupt and gradual switching characteristics within a single device, addressing the demands for lower latency and energy consumption for binary switching applications, and graduality for neuromorphic computing applications. We evaluated the underlying principles of both switching modes, attributing the anomalous gradual switching to the modulation of oxygen-deficient layers formed between the active electrode and oxide switching layer. The memristive cell (1R) was integrated with 40 nm transistor technology (1T) to form a 1T-1R memory cell, demonstrating a switching speed of 50 ns with a pulse amplitude of ±2.5 V in its forward-biased mode. Applying pulse trains of 20 ns to 490 ns in the reverse-biased mode exhibited synaptic weight properties, obtaining a nonlinearity (NL) factor of <0.5 for both potentiation and depression. The devices in both modes also demonstrated an endurance of >106 cycles, and their conductance states were also stable under temperature stress at 85 °C for 104 s. With the duality of the two switching modes, our device can be used for both memory and synaptic weight-storing applications.
Despite impressive demonstrations of memristive behavior with halide perovskites, no clear pathway for material and device design exists for their applications in neuromorphic computing. Present approaches are limited to single element structures, fall behind in terms of switching reliability and scalability, and fail to map out the analog programming window of such devices. Here, we systematically design and evaluate robust pyridinium-templated one-dimensional halide perovskites as crossbar memristive materials for artificial neural networks. We compare two halide perovskite 1D inorganic lattices, namely (propyl)pyridinium and (benzyl)pyridinium lead iodide. The absence of conjugated, electron-rich substituents in PrPyr+ prevents edge-to-face type π-stacking, leading to enhanced electronic isolation of the 1D iodoplumbate chains in (PrPyr)[PbI3], and hence, superior resistive switching performance compared to (BnzPyr)[PbI3]. We report outstanding resistive switching behaviours in (PrPyr)[PbI3] on the largest flexible crossbar implementation (16 × 16) to date - on/off ratio (>105), long term retention (105 s) and high endurance (2000 cycles). Finally, we put forth a universal approach to comprehensively map the analog programming window of halide perovskite memristive devices - a critical prerequisite for weighted synaptic connections in artificial neural networks. This consequently facilitates the demonstration of accurate handwritten digit recognition from the MNIST database based on spike-timing-dependent plasticity of halide perovskite memristive synapses.
Hardware neural networks with mechanical flexibility are promising next-generation computing systems for smart wearable electronics. Overcoming the challenge of developing a fully synaptic plastic network, we demonstrate a low-operating-voltage PET/ITO/p-MXene/Ag flexible memristor device by controlling the etching of aluminum metal ions in Ti3C2Tx MXene. The presence of a small fraction of Al ions in partially etched MXene (p-Ti3C2Tx) significantly suppresses the operating voltage to 1 V compared to 7 V from fully Al etched MXene (f-Ti3C2Tx)-based devices. Former devices exhibit excellent non-volatile data storage properties, with a robust â¼103 ON/OFF ratio, high endurance of â¼104 cycles, multilevel resistance states, and long data retention measured up to â¼106 s. High mechanical stability up to â¼73° bending angle and environmental robustness are confirmed with consistent switching characteristics under increasing temperature and humid conditions. Furthermore, a p-Ti3C2Tx MXene memristor is employed to mimic the biological synapse by measuring the learning-forgetting pattern for â¼104 cycles as potentiation and depression. Spike time-dependent plasticity (STDP) based on Hebb's Learning rules is also successfully demonstrated. Moreover, a remarkable accuracy of â¼95% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) data set with just 29 training epochs is achieved in simulation. Ultimately, our findings underscore the potential of MXene-based flexible memristor devices as versatile components for data storage and neuromorphic computing.
Background and purpose: High-density dental fillings pose a non-negligible impact on head and neck cancer treatment. For proton therapy, stopping power ratio (SPR) prediction will be significantly impaired by the associated image artifacts. Dose perturbation is also inevitable, compromising the treatment plan quality. While plenty of work has been done on metal or amalgam fillings, none has touched on composite resin (CR) and glass ionomer cement (GIC) which have seen an increasing usage. Hence, this work aims to provide a detailed characterisation of SPR and dose perturbation in proton therapy caused by CR and GIC. Materials and methods: Four types of fillings were used: CR, Fuji Bulk (FB), Fuji II (FII) and Fuji IX (FIX). The latter three belong to GIC category. Measured SPR were compared with SPR predicted using single-energy computed tomography (SECT) and dual-energy computed tomography (DECT). Dose perturbation of proton beams with lower- and higher-energy levels was also quantified using Gafchromic films. Results: The measured SPR for CR, FB, FII and FIX were 1.68, 1.77, 1.77 and 1.76, respectively. Overall, DECT could predict SPR better than SECT. The lowest percentage error achieved by DECT was 19.7 %, demonstrating the challenge in estimating SPR, even for fillings with relatively lower densities. For both proton beam energies and all four fillings of about 4.5 mm thickness, the maximum dose perturbation was 3 %. Conclusion: This study showed that dose perturbation by CR and GIC was comparatively small. We have measured and recommended the SPR values for overriding the fillings in TPS.
Spin-based memory technology is now available as embedded magnetic random access memory (eMRAM) for fast, high-density and non-volatile memory products, which can significantly boost computing performance and ignite the development of new computing architectures.
Neuromorphic platforms are gaining popularity due to their superior efficiency, low power consumption, and adaptable parallel signal processing capabilities, overcoming the limitations of traditional von Neumann architecture. We conduct an in-depth investigation into the factors influencing the resistive switching mechanism in memristor devices utilizing lead iodide (PbI2). We establish correlations between device performance and morphological features, unveiling synaptic like behaviour of device making it suitable for range of flexible neuromorphic applications. Notably, a highly reliable unipolar switching mechanism is identified, exhibiting stability even under mechanical strain (with a bending radius of approximately 4 mm) and in high humidity environment (at 75% relative humidity) without the need for encapsulation. The investigation delves into the complex interplay of charge transport, ion migration and the active interface, elucidating the factors contributing to the remarkable resistive switching observed in PbI2-based memristors. The detailed findings highlight synaptic behaviors akin to the modulation of synaptic strengths, with an impressive potentiation and depression of 2 × 104 cycles, emphasizing the role of spike time-dependent plasticity (STDP). The flexible platform demonstrates exceptional performance, achieving a simulated accuracy rate of 95.06% in recognizing modified patterns from the National Institute of Standards and Technology (MNIST) dataset with just 30 training epochs. Ultimately, this research underscores the potential of PbI2-based flexible memristor devices as versatile component for neuromorphic computing. Moreover, it demonstrate the robustness of PbI2 memristors in terms of their resistive switching capabilities, showcasing resilience both mechanically and electrically. This underscores their potential in replicating synaptic functions for advanced information processing systems.
Due to their significant resemblance to the biological brain, spiking neural networks (SNNs) show promise in handling spatiotemporal information with high time and energy efficiency. Two-terminal memristors have the capability to achieve both synaptic and neuronal functions; however, such memristors face asynchronous programming/reading operation issues. Here, a three-terminal memristor (3TM) based on oxygen ion migration is developed to function as both a synapse and a neuron. We demonstrate short-term plasticity such as pair-pulse facilitation and high-pass dynamic filtering in our devices. Additionally, a 'learning-forgetting-relearning' behavior is successfully mimicked, with lower power required for the relearning process than the first learning. Furthermore, by leveraging the short-term dynamics, the leaky-integrate-and-fire neuronal model is emulated by the 3TM without adopting an external capacitor to obtain the leakage property. The proposed bi-functional 3TM offers more process compatibility for integrating synaptic and neuronal components in the hardware implementation of an SNN.
Neural Networks, Computer , Neuronal Plasticity , Neuronal Plasticity/physiology , Neurons/physiology , Synapses , Brain
Conventional magnetic nanoagents in cancer hyperthermia therapy suffer from a low magnetic heating efficiency. To address this issue, researchers have pursued magnetic nanoparticles with topological magnetic domain structures, such as the vortex-domain structure, to enhance the magnetic heating performance of conventional nanoparticles while maintaining excellent biocompatibility. In this study, we synthesized hollow spherical Mn0.5Zn0.5Fe2O4 (MZF-HS) nanoparticles using a straightforward solvothermal method, yielding samples with an average outer diameter of approximately 350 nm and an average inner diameter of about 220 nm. The heating efficiency of the nanoparticles was experimentally verified, and the specific absorption rate (SAR) value of the hollow MZF was found to be approximately 1.5 times that of solid MZF. The enhanced heating performance is attributed to the vortex states in the hollow MZF structure as validated with micromagnetic simulation studies. In vitro studies demonstrated the lower cell viability of breast cancer cells (MCF-7, BT549, and 4T1) after MHT in the presence of MZF-HS. The synthesized MZF caused 51% cell death after MHT, while samples of MZF-HS resulted in 77% cell death. Our findings reveal that magnetic particles with a vortex state demonstrate superior heating efficiency, highlighting the potential of hollow spherical particles as effective heat generators for MHT applications.
Hyperthermia, Induced , Nanoparticles , Nanoparticles/chemistry , Magnetics , Hyperthermia, Induced/methods , Magnetic Phenomena , Zinc
We investigate the functionality of NbOx-based selector devices on a flexible substrate. It was observed that the failure mechanism of cyclic tensile strain is from the disruption of atom arrangements, which essentially led to the crack formation of the film. When under cyclic compressive strain, buckling delamination of the film occurs as the compressed films have debonded from their neighboring layers. By implementing an annealing process after the strain-induced degradation, recovery of the device is observed with reduced threshold and hold voltages. The physical mechanism of the device is investigated through Poole-Frenkel mechanism fitting, which provides insights into the switching behavior after mechanical strain and annealing process. The result demonstrates the potential of the NbOx device in flexible electronics applications with a high endurance of up to 105 cycles of cyclic bending strain and the recovery of the device after degradation.
Emerging technologies, i.e., spintronics, 2D materials, and memristive devices, have been widely investigated as the building block of neuromorphic computing systems. Three-terminal memristor (3TM) is specifically designed to mitigate the challenges encountered by its two-terminal counterpart as it can concurrently execute signal transmission and memory operations. In this work, we present a complementary metal-oxide-semiconductor-compatible 3TM with highly linear weight update characteristics and a dynamic range of â¼15. The switching mechanism is governed by the migration of oxygen ions and protons in and out of the channel under an external gate electric field. The involvement of the protonic defects in the electrochemical reactions is proposed based on the bipolar pulse trains required to initiate the oxidation process and the device electrical characteristics under different humidity levels. For the synaptic operation, an excellent endurance performance with over 256k synaptic weight updates was demonstrated while maintaining a stable dynamic range. Additionally, the synaptic performance of the 3TM is simulated and implemented into a four-layer neural network (NN) model, achieving an accuracy of â¼92% in MNIST handwritten digit recognition. With such desirable conductance modulation characteristics, our proposed 3T-memristor is a promising synaptic device candidate to realize the hardware implementation of the artificial NN.
The readout margin of the one selector-one RRAM crossbar array architecture is strongly dependent on the nonlinearity of the selector device. In this work, we demonstrated that the nonlinearity of Pt/TiO2/Pt exponential selectors increases with decreasing oxygen vacancy defect density. The defect density is controlled by modulating the sputtering pressure in the oxide deposition process. Our results reveal that the dominant conduction mechanisms of the Pt/TiO2/Pt structure transit from Schottky emission to Poole-Frenkel emission with the increase of sputtering pressure. Such transition is attributed to the rise of oxygen vacancy concentration. In addition, the short-term plasticity feature of the Pt/TiO2/Pt selector is shown to be enhanced with a lower defect density. These results suggest that low defect density is necessary for improved exponential selector performances.
The use of crystalline metal-organic complexes with definite structures as multilevel memories can enable explicit structure-property correlations, which is significant for designing the next generation of memories. Here, four Zn-polysulfide complexes with different degrees of conjugation have been fabricated as memory devices. ZnS6(L)2-based memories (L = pyridine and 3-methylpyridine) can exhibit only bipolar binary memory performances, but ZnS6(L)-based memories (L = 2,2'-bipyridine and 1,10-phenanthroline) illustrate non-volatile ternary memory performances with high ON2/ON1/OFF ratios (104.22/102.27/1 and 104.85/102.58/1) and ternary yields (74% and 78%). Their ON1 states stem from the packing adjustments of organic ligands upon the injection of carriers, and the ON2 states are a result of the ring-to-chain relaxation of S62- anions. The lower conjugated degrees in ZnS6(L)2 result in less compact packing; consequently, the adjacent S62- rings are too long to trigger the S62- relaxation. The deep structure-property correlation in this work provides a new strategy for implementing multilevel memory by triggering polysulfide relaxation based on the conjugated degree regulation of organic ligands.
This work demonstrates oscillation frequency modulation in a NbO2-based relaxation oscillator device, in which the oscillation frequency increases with operating temperature and source voltage, and decreases with load resistance. An annealing-induced oxygen diffusion at 373 K was carried out to optimize the stoichiometry of the bulk NbO2to achieve consistent oscillation frequency shift with device temperature. The device exhibits stable self-sustained oscillation in which the frequency can be modulated between 2 and 33 MHz, and a wider operating voltage range can be obtained. An additional surface treatment step was employed during fabrication to reduce the surface roughness of the bottom electrode and to remove surface contaminants that affect the interfacial properties of the device. The device frequency tunability coupled with high oscillating frequency and high endurance capability of more than 1.5 × 108cycles indicates that the Pt/NbO2/Pt device is particularly suitable for applications in an oscillatory neural network.
Short-term plasticity (STP) is an important synaptic characteristic in the hardware implementation of artificial neural networks (ANN), as it enables the temporal information processing (TIP) capability. However, the STP feature is rather challenging to reproduce from a single nonvolatile resistive random-access memory (RRAM) element, as it requires a certain degree of volatility. In this work, a Pt/TiOx/Pt exponential selector is introduced not only to suppress the sneak current but also to enable the TIP feature in a one selector-one RRAM (1S1R) synaptic device. Our measurements reveal that the exponential selector exhibits the STP characteristic, while a Pt/HfOx/Ti RRAM enables the long-term memory capability of the synapse. Thereafter, we experimentally demonstrated pulse frequency-dependent multilevel switching in the 1S1R device, exhibiting the TIP capability of the developed 1S1R synapse. The observed STP of the selector is strongly influenced by the bottom metal-oxide interface, in which Ar plasma treatment on the bottom Pt electrode resulted in the annihilation of the STP feature in the selector. A mechanism is thus proposed to explain the observed STP, using the local electric field enhancement induced at the metal-oxide interface coupled with the drift-diffusion model of mobile O2- and Ti3+ ions. This work therefore provides a reliable means of producing the STP feature in a 1S1R device, which demonstrates the TIP capability sought after in hardware-based ANN.
Field-free magnetization switching is critical towards practical, integrated spin-orbit torque (SOT)-driven magnetic random-access memory with perpendicular magnetic anisotropy. Our work proposes a technique to modulate the spin reflection and spin density of states within a heavy-metal Pt through interfacing with a dielectric MgO layer. We demonstrate tunability of the effective out-of-plane spin torque acting on the ferromagnetic Co layer, enabling current-induced SOT magnetization switching without the assistance of an external magnetic field. The influence of the MgO layer thickness on effective SOT efficiency shows saturation at 4 nm, while up to 80% of field-free magnetization switching ratio is achieved with the MgO between 5 and 8 nm. We analyze and attribute the complex interaction to spin reflection at the dielectric/heavy metal interface and spin scattering within the dielectric medium due to interfacial electric fields. Further, through substituting the dielectric with Ti or Pt, we confirm that the MgO layer is indeed responsible for the observed field-free magnetization switching mechanism.
3D printing via vat photopolymerization (VP) is a highly promising approach for fabricating magnetic soft millirobots (MSMRs) with accurate miniature 3D structures; however, magnetic filler materials added to resin either strongly interfere with the photon energy source or sediment too fast, resulting in the nonuniformity of the filler distribution or failed prints, which limits the application of VP. To this end, a circulating vat photopolymerization (CVP) platform that can print MSMRs with high uniformity, high particle loading, and strong magnetic response is presented. After extensive characterization of materials and 3D printed parts, it is found that SrFe12 O19 is an ideal magnetic filler for CVP and can be printed with 30% particle loading and high uniformity. By using CVP, various tethered and untethered MSMRs are 3D printed monolithically and demonstrate the capability of reversible 3D-to-3D transformation and liquid droplet manipulation in 3D, an important task for in vitro diagnostics that are not shown with conventional MSMRs. A fully automated liquid droplet handling platform that manipulates droplets with MSMR is presented for detecting carbapenem antibiotic resistance in hazardous biosamples as a proof of concept, and the results agree with the benchmark.
Magnetic Phenomena , Printing, Three-Dimensional , Physical Phenomena
PURPOSE: Relative biological effectiveness (RBE) uncertainties have been a concern for treatment planning in proton therapy, particularly for treatment sites that are near organs at risk (OARs). In such a clinical situation, the utilization of variable RBE models is preferred over constant RBE model of 1.1. The problem, however, lies in the exact choice of RBE model, especially when current RBE models are plagued with a host of uncertainties. This paper aims to determine the influence of RBE models on treatment planning, specifically to improve the understanding of the influence of the RBE models with regard to the passing and failing of treatment plans. This can be achieved by studying the RBE-weighted dose uncertainties across RBE models for OARs in cases where the target volume overlaps the OARs. Multi-field optimization (MFO) and single-field optimization (SFO) plans were compared in order to recommend which technique was more effective in eliminating the variations between RBE models. METHODS: Fifteen brain tumor patients were selected based on their profile where their target volume overlaps with both the brain stem and the optic chiasm. In this study, 6 RBE models were analyzed to determine the RBE-weighted dose uncertainties. Both MFO and SFO planning techniques were adopted for the treatment planning of each patient. RBE-weighted dose uncertainties in the OARs are calculated assuming ( α ß ) x of 3 Gy and 8 Gy. Statistical analysis was used to ascertain the differences in RBE-weighted dose uncertainties between MFO and SFO planning. Additionally, further investigation of the linear energy transfer (LET) distribution was conducted to determine the relationship between LET distribution and RBE-weighted dose uncertainties. RESULTS: The results showed no strong indication on which planning technique would be the best for achieving treatment planning constraints. MFO and SFO showed significant differences (P <.05) in the RBE-weighted dose uncertainties in the OAR. In both clinical target volume (CTV)-brain stem and CTV-chiasm overlap region, 10 of 15 patients showed a lower median RBE-weighted dose uncertainty in MFO planning compared with SFO planning. In the LET analysis, 8 patients (optic chiasm) and 13 patients (brain stem) showed a lower mean LET in MFO planning compared with SFO planning. It was also observed that lesser RBE-weighted dose uncertainties were present with MFO planning compared with SFO planning technique. CONCLUSIONS: Calculations of the RBE-weighted dose uncertainties based on 6 RBE models and 2 different ( α ß ) x revealed that MFO planning is a better option as opposed to SFO planning for cases of overlapping brain tumor with OARs in eliminating RBE-weighted dose uncertainties. Incorporation of RBE models failed to dictate the passing or failing of a treatment plan. To eliminate RBE-weighted dose uncertainties in OARs, the MFO planning technique is recommended for brain tumor when CTV and OARs overlap.
Magnetically directed localized polymerization is of immense interest for its extensive impacts and applications in numerous fields. The use of means untethered from an external magnetic field to localize initiation of polymerization to develop a curing system is a novel concept, with a sustainable, efficient, and eco-friendly approach and a wide range of potential in both science and engineering. However, the conventional means for the initiation of polymerization cannot define the desirable location of polymerization, which is often exacerbated by the poor temporal control in the curing system. Herein, the copper-immobilized dendrimer-based magnetic iron oxide silica (MNPs-G2@Cu2+) co-nanoinitiators are rationally designed as initiators for redox radical polymerization. The nanoinitiators are magnetically responsive and therefore enable localized polymerization using an external magnetic field. In this work, anaerobic polymerization of an adhesive composed of triethylene glycol dimethacrylate, tert-butyl peroxybenzoate, and MNPs-G2@Cu2+ as the magnetic co-nanoinitiators has been investigated. The use of a magnet locates and promotes redox free radical polymerization through the synergistic functions between peroxide and MNPs-G2@Cu2+ co-nanoinitiators. The mechanical properties of the resulting polymer are considerably reinforced because the MNPs-G2@Cu2+ co-nanoinitiators concurrently play another crucial role as nanofillers. This strategy provides a novel approach for magnetically tunable localized polymerization, which allows new opportunities to govern the formulation of advanced adhesives through polymerization under hazard-free conditions for various promising applications.
