Rafael Ramos

Realizing the potential of oxide-based memristive devices for high-density data storage and energy-efficient computing still relies on overcoming key technical challenges, including the need for a larger number of stable resistance states, faster switching speeds, lower SET/RESET voltages, improved endurance, and reduced variability. Addressing these limitations requires innovative material design strategies. Here, we demonstrate that introducing a thin layer of oxide-ion conductor SrCoO3–x between the metal and the SrTiO3-based memristive elements, expands the number of distinguishable resistance states from ≈8 to ≈22. This modification also reduced the SET/RESET voltage by 50% and markedly improved device endurance, albeit with a trade-off of reduced state retention. To assess the performance of this architecture, we trained a two-layer fully connected neural network using the experimental SrTiO3/SrCoO3–x memristor characteristics on the MNIST handwritten digit dataset. Networks with hidden-layer sizes between 64 and 256 neurons achieved classification errors below 7%. Finally, we confirmed the transferability of this interface-engineering approach by applying it to HfOx-based devices, achieving a consistent enhancement in the resistive state window.

Electrically conductive two‐dimensional covalent organic frameworks (2D COFs) have emerged as a versatile class of crystalline porous polymers with promising applications in electronics and energy storage. However, high electrical conductivity generally relies on post‐synthetic doping to generate charge carriers, which can compromise crystallinity, porosity, and structural homogeneity. Persistent neutral radical conductors provide an attractive alternative, as their unpaired electrons can generate free charge carriers without the need for counterions. Nevertheless, the incorporation of highly spin‐delocalized π‐radicals into COFs remains largely unexplored. Herein, we report the design and synthesis of imine‐linked 2D COFs incorporating spin‐delocalized trioxotriangulene (TOT) neutral radicals through complementary synthetic approaches. Direct use of a TOT radical derivative bearing amino groups affords a highly crystalline framework (TOT‐COF‐H) that exhibits semiconducting behavior ( σ _RT = 1.2 × 10 ⁻⁴ S cm ⁻¹ ), a reduced band gap ( E _g = 1.09 eV), and a low activation energy (0.24 eV). This work demonstrates a viable strategy for integrating spin‐delocalized neutral radical building blocks into COFs, enabling the development of intrinsically conductive, porous, and crystalline organic frameworks without the need for extrinsic doping.

Electrically conductive two‐dimensional covalent organic frameworks (2D COFs) have emerged as a versatile class of crystalline porous polymers with promising applications in electronics and energy storage. However, high electrical conductivity generally relies on post‐synthetic doping to generate charge carriers, which can compromise crystallinity, porosity, and structural homogeneity. Persistent neutral radical conductors provide an attractive alternative, as their unpaired electrons can generate free charge carriers without the need for counterions. Nevertheless, the incorporation of highly spin‐delocalized π‐radicals into COFs remains largely unexplored. Herein, we report the design and synthesis of imine‐linked 2D COFs incorporating spin‐delocalized trioxotriangulene (TOT) neutral radicals through complementary synthetic approaches. Direct use of a TOT radical derivative bearing amino groups affords a highly crystalline framework (TOT‐COF‐H) that exhibits semiconducting behavior ( σ _RT = 1.2 × 10 ⁻⁴ S cm ⁻¹ ), a reduced band gap ( E _g = 1.09 eV), and a low activation energy (0.24 eV). This work demonstrates a viable strategy for integrating spin‐delocalized neutral radical building blocks into COFs, enabling the development of intrinsically conductive, porous, and crystalline organic frameworks without the need for extrinsic doping.

The development of high‐capacity, sustainable cathode materials remains a critical challenge in advancing lithium‐ion battery technologies for next‐generation energy storage. Organic electrode materials (OEMs) represent a promising alternative to conventional inorganic cathodes, owing to their composition from earth‐abundant elements and chemically tunable structures that enable high theoretical capacities. Herein, a polyimide‐linked porous organic polymer (HAT‐PTO) is reported to be synthesized via a straightforward hydrothermal reaction from redox‐active hexaazatriphenylene (HAT) and pyrene‐4,5,9,10‐tetraone (PTO) building blocks. The resulting HAT‐PTO framework incorporates multiple redox‐active C═O and C═N centers, delivering a high theoretical capacity of 484 mAh g⁻¹. To overcome limitations in electronic conductivity, hybrid materials are synthesized by in situ growth of HAT‐PTO on multiwalled pristine (CNT) and carboxyl‐functionalized carbon nanotubes (cCNT). Notably, the HAT‐PTO‐cCNT hybrid delivers a high capacity of 397 mAh g⁻¹ at C/10, outstanding rate capability of 225 mAh g⁻¹ at 20 C, and long‐term cycling stability, retaining 171 mAh g⁻¹ after 6000 cycles at 2 C. Ex situ FT‐IR, supported by density functional theory (DFT) calculations, confirms the involvement of both HAT and PTO units in the charge storage mechanism. This work presents a molecular design strategy and scalable synthesis approach toward high‐performance organic cathodes, paving the way for durable, high‐rate lithium‐organic batteries.

The use of magnetic garnets in new technologies such as spintronic devices requires fine-structured thin films. Classical fabrication techniques for these materials, typically physical vapor deposition methods, lead to excellent magnetic behavior. However, availability and scalability for potential applications are well restricted. In this study, we propose an innovative approach to fabricating Yttrium Iron Garnet thin films with precise thickness control achieved through iterative layer deposition via a chemical synthesis route. Remarkably, the iterative deposition process results in films exhibiting exceptional crystallinity. Magnetic characterization provides saturation magnetization and coercivity values on par with those reported in literature, summed to narrow ferromagnetic resonance lines. Therefore, in this work we demonstrate the viability of polymer assisted deposition as a promising alternative thinking about scalability to conventional deposition techniques for this material. Notably, our findings reveal energy conversion efficiencies comparable to those achieved with materials synthesized via physical vapor deposition methods.

The operation of oxide-based memristive devices relies on the fast accumulation and depletion of oxygen vacancies by an electric field close to the metal-oxide interface. Here, we show that the reversible change of the local concentration of oxygen vacancies at this interface also produces a change in the thermal boundary resistance (TBR), i.e., a thermal resistive switching effect. We used frequency domain thermoreflectance to monitor the interfacial metal-oxide TBR in (Pt,Cr)/SrTiO₃ devices, showing a change of ≈20% under usual SET/RESET operation voltages, depending on the structure of the device. Time-dependent thermal relaxation experiments suggest ionic rearrangement along the whole area of the metal/oxide interface, apart from the ionic filament responsible for the electrical conductivity switching. The experiments presented in this work provide valuable knowledge about oxide ion dynamics in redox-based memristive devices.

When the electric conductance of a nano-sized metal is measured at low temperatures, it often exhibits complex but reproducible patterns as a function of external magnetic fields called quantum fingerprints in electric conductance. Such complex patterns are due to quantum–mechanical interference of conduction electrons; when thermal disturbance is feeble and coherence of the electrons extends all over the sample, the quantum interference pattern reflects microscopic structures, such as crystalline defects and the shape of the sample, giving rise to complicated interference. Although the interference pattern carries such microscopic information, it looks so random that it has not been analysed. Here we show that machine learning allows us to decipher quantum fingerprints; fingerprint patterns in magneto-conductance are shown to be transcribed into spatial images of electron wave function intensities (WIs) in a sample by using generative machine learning. The output WIs reveal quantum interference states of conduction electrons, as well as sample shapes. The present result augments the human ability to identify quantum states, and it should allow microscopy of quantum nanostructures in materials by making use of quantum fingerprints.