Solid-state spin defects provide a versatile platform for quantum sensing with nanoscale spatial resolution and room-temperature operation. Spin defects in diamond have enabled mature scanning-probe devices, while related defects in silicon carbide and hexagonal boron nitride are being actively explored for scalable sensing platforms. However, the sensitivity of practical and scanning-probe devices remains below that of optimized bulk systems. Although optical fluorescence detection is widely used, practical performance is often constrained by limitations in signal acquisition and readout efficiency, motivating continued efforts to improve readout technologies. This review surveys material platforms and optical and photoelectrical readout technologies for solid-state spin defects. We compare fluorescence- and photoelectric-based detection schemes in terms of readout fidelity, sensitivity, and scalability, and discuss how materials properties and carrier transport influence practical performance. These perspectives provide guidelines for improving readout efficiency and advancing high-sensitivity quantum sensors and scanning probes.
We developed a precise molecular layer deposition technique using pulsed laser deposition (PLD) to realize a quantum cascade laser (QCL) structure using wide-bandgap semiconductors ZnO and Mg x Zn1-x O. Compared to GaAs and InP, ZnO has a larger optical bandgap (3.3 eV) and higher longitudinal optical phonon energy (72 meV). This combination suppresses leakage current and promotes fast nonradiative relaxation, making ZnO a promising material for high-temperature QCL devices operating near room temperature. Nanometer-order thickness control is essential for realizing a QCL device structure. However, PLD techniques suffer from fluctuations in the deposition rate caused by viewport contamination. In this study, a quartz crystal microbalance is used to continuously measure the deposition mass during the PLD process. The combination of a cooling and shielding design with an infrared heating system is used to suppress the thermal drift and plasma damage. Consequently, precise molecular layer control is achieved in the periodic structure of the ZnO/Mg x Zn1-x O superlattice. The prepared ZnO QCL structure is characterized by scanning transmission electron microscopy and X-ray diffraction, which confirms that the designed superstructure is reproduced with high precision. This study establishes a PLD process to realize ZnO-based QCL and paves the way for developing new QCL platforms using wide-bandgap materials.
This paper reviews recent advances in signal processing and resistive switching mechanisms of niobium oxides. The reported signal-processing functionalities include neural spike firing, phase locking, frequency and amplitude modulation, information-flux control, and quantized encoding. The underlying resistive switching mechanism indicates that the phase composition within conductive filaments, or the local activity of niobium oxides, can be modulated and stabilized by acquiring appropriate energy from external electrical stimuli. Precise control over these two characteristics enables the design of artificial neural networks with high biological plausibility, thereby significantly enhancing computational efficiency.
Janus transition metal dichalcogenide (TMD) monolayers offer distinctive physical properties and device applications because their broken out-of-plane mirror symmetry induces an intrinsic out-of-plane dipole. High crystal quality is essential for accessing their intrinsic excitonic physics, yet improving Janus TMD quality remains challenging due to structural degradation and strain introduced during chalcogen substitution. To address this challenge, we develop a hexagonal boron nitride (hBN)-supported chalcogen substitution process that yields crack-free Janus TMD monolayers with improved optical uniformity. MoSe2 and WSe2 monolayers are first grown on hBN substrates and then converted into Janus MoSSe and WSSe monolayers via room-temperature H2 plasma treatment, respectively. Compared with conventional SiO2-supported samples, the hBN-supported process yields Janus monolayers with reduced inhomogeneous lattice strain, owing to negligible in-plane interactions with the substrate. The hBN-supported samples exhibit narrow photoluminescence (PL) linewidths of 40–50 meV at room temperature, which further decrease to ~9 meV at 5 K. This linewidth reduction enables quantitative analysis of the trion binding energy and exciton–phonon interaction and provides a practical route to accessing intrinsic properties of Janus TMDs.
Suspension cells are widely used in biomedical research and cell-based therapies; however, their lack of stable adhesion to substrates limits their efficient handling, including gene delivery and downstream processing. Although surface engineering approaches have been studied to induce cell adhesion, precise control over attachment and detachment, comparable to that of adherent cells, remains challenging. Here, we report a strategy to control cell adhesion and detachment using enzyme-responsive trans-activator of transcription peptide-poly(ethylene glycol)-lipid (Tat-PEG-lipid) conjugates and non-functional poly(ethylene glycol)-lipids (PEG-lipid). A collagenase-cleavable peptide sequence was inserted between the Tat peptide and the PEG chain, yielding Tat-Col-PEG-lipid constructs with PEG molecular weights of 20 and 40 kDa. These conjugates were incorporated into the cell membranes of CCRF-CEM cells, a human T-lymphoblastoid cell line, to induce adhesion via the Tat peptide. To regulate the interfacial structure, Tat-Col-PEG-lipid was co-assembled with a non-functional PEG(5k)-lipid, enabling precise control over the surface density to improve collagenase access. We demonstrated that the mixed PEG-lipid modification allows robust cell adhesion to substrates while enabling efficient detachment upon collagenase treatment. Notably, the use of Tat-Col-PEG-lipids with longer PEG chains (40 kDa) significantly improved the adhesion efficiency, enzymatic detachment, and cell viability compared to shorter PEG chains. Optimal mixing ratios of Tat-Col-PEG(40k)-lipids and PEG(5k)-lipids resulted in stable attachment and rapid collagenase-triggered release across different substrates, planar surfaces, and fibrous scaffolds. This hierarchical PEG-lipid modification approach provides a versatile and minimally invasive platform for the transient manipulation of suspended cells, with potential applications in gene delivery, cell processing, and regenerative medicine.
One of the most formidable challenges in materials chemistry is the rational design of functionalities capable of dramatically enhancing performance. However, it is well-known that the discovery of promising materials often requires several decades of continuous trial-and-error. Herein, we show an interpretable data-driven framework for the discovery of multielement metal oxide oxygen evolution reaction (OER) electrocatalysts in alkaline media within a substantially shorter timeframe. This framework was trained on a hybrid dataset comprising only 557 data, consisting of the curated literature and our own experimental results. Furthermore, this framework was specifically designed to enable extrapolative materials discovery, including the exploration of elemental combinations absent from the training database. Consequently, from a large material search space of approximately 3 million candidates, we identified a promising unconventional quinary oxide composed of V, Ni, W, Rh, and Ru that exhibits, in 0.1 M KOH, an exchange current density approximately 20 times higher than that of IrO2. This work serves as a proof-of-concept, demonstrating that the rational design of high-performance electrochemical functionalities from an extensive candidate space can be achieved using a small hybrid dataset combined with an interpretable data-driven approach.
High-pressure techniques provide a powerful approach for exploring superconducting materials by enabling structural modifications that significantly alter electronic and phonon properties. In this study, we investigate the emergence of superconductivity in Sn3Se4 and Ge3S4 with the Th3P4-type cubic structure using high-pressure synthesis and in situ electrical transport measurements in a diamond anvil cell equipped with boron-doped diamond electrodes. Both compounds exhibit superconductivity, with maximum transition temperature T c of 7.9 K at 8 GPa for Sn3Se4 and 10.5 K at 11 GPa for Ge3S4. The behavior of the T c as a function of pressure and previously reported theoretical calculations, including related compounds, suggest a phonon-mediated BCS-type mechanism for observed superconductivity. These results expand the family of Th3P4-type superconductors and demonstrate the effectiveness of high-pressure techniques for discovering new superconducting phases.
Accurate determination of the chiral indices of carbon nanotubes (CNTs) is crucial for their controllable synthesis and practical applications. Transmission electron microscopy (TEM) combined with electron diffraction has been one of the most reliable methods for characterizing the chiral indices of CNTs. However, it is still a challenge to analyze TEM images to extract chirality with high efficiency and accuracy, especially for nanotubes with a diameter larger than 2 nanometers. In this work, a Python code is developed with assistance from an artificial intelligence model (Claude Code) for the auto-extraction of CNTs' chirality from TEM images. Precise and reliable measurement of the diameter was realized by using a new method to determine the average distance between the bright and dark fringes. Symmetry and geometry-based rules enabled measurements of layer lines in the Fourier transformation of TEM images with a sub-pixel resolution. As a result, an accuracy higher than 90% for CNTs with diameters ranging from 0.5 nm to 3.0 nm was achieved. High robustness has been validated by experimentally analyzing the chiralities of CNTs grown by a floating catalyst chemical vapor deposition method. The open-source codes and tools will be valuable for the research community to quantitatively identify the chirality distribution of CNTs with high efficiency.
We developed a precise molecular layer deposition technique using pulsed laser deposition (PLD) to realize a quantum cascade laser (QCL) structure using wide-bandgap semiconductors ZnO and MgxZn1-xO. Compared to GaAs and InP, ZnO has a larger optical bandgap (3.3 eV) and higher longitudinal optical phonon energy (72 meV). This combination suppresses leakage current and promotes fast nonradiative relaxation, making ZnO a promising material for high-temperature QCL devices operating near room temperature. Nanometer-order thickness control is essential for realizing a QCL device structure. However, PLD techniques suffer from fluctuations in the deposition rate caused by viewport contamination. In this study, a quartz crystal microbalance is used to continuously measure the deposition mass during the PLD process. The combination of a cooling and shielding design with an infrared heating system is used to suppress the thermal drift and plasma damage. Consequently, precise molecular layer control is achieved in the periodic structure of the ZnO/MgxZn1-xO superlattice. The prepared ZnO QCL structure is characterized by scanning transmission electron microscopy and X-ray diffraction, which confirms that the designed superstructure is reproduced with high precision. This study establishes a PLD process to realize ZnO-based QCL and paves the way for developing new QCL platforms using wide-bandgap materials.
Aqueous organic redox flow batteries (AORFBs) offer significant potential for grid-scale storage of renewable energy. However, their commercial viability is often limited by the chemical instability of the organic electrolytes. Quantifying and assessing the effects of the various degradation mechanisms from a molecular design perspective is very challenging both experimentally and computationally. Here, we propose the use of simple thermodynamic descriptors for seven degradation mechanisms for a diverse virtual library of ca. 2000 monofunctionalized quinones built from seven core structures. In the process, we developed a cheminformatics-based workflow that can reliably and automatically generate reactants and mechanism-specific degradation products. Using DFT-calculated reaction Gibbs free energies as thermodynamic descriptors and a calibrated model for redox-potential prediction, we systematically analyse seven degradation mechanisms across this library. We find clear relationships between redox potential and degradation thermodynamics for six of the seven mechanisms, with opposite trend directions for oxidized and reduced forms, revealing stability - potential trade-offs that constrain molecular design. The analysis further shows how functional groups modulate degradation thermodynamics and thereby helps rationalize the relative stability of anthraquinone-based outliers. Finally, redox active molecules from recent experimental studies are evaluated within the proposed thermodynamics-based framework, and we comment on the implications of electrochemical reversibility of some critical degradation mechanisms. Overall, this work provides a physically motivated framework for the multi-objective screening of quinone-based AORFB electrolytes and helps clarify the design criteria needed to identify favourable molecular outliers.
Cr2TiAlC2 MAX phase was synthesized for the first time by reactive spark plasma sintering (SPS) from pure metallic precursors. A systematic study of each SPS parameter was performed to promote the formation of the pure phase. The transport properties of the resulting pellet were probed and compared to a conventionally synthesized sample made from pure Cr2TiAlC2 densified powder. The electrical conductivity was measured to be 8.31 × 105 S.m-1 at 298 K, while the thermal conductivity was measured to be 11.76 W.m-1.K-1 at 298 K, one of the lowest values reported for a MAX phase. The measured Vickers hardness was 7.62 ± 0.16 GPa at a load of 19.8 N, which is about 40% higher than samples synthesized by hot pressing, making it the hardest 312 MAX phase reported to date. Although Cr2TiAlC2 was confirmed to be a poor thermoelectric compound, exhibiting a low Seebeck coefficient of -4.2 μV.K-1 at 298 K. These results provide a baseline for comparing structural and transport properties prior to etching into MXenes, thereby helping to better understand the influence of dimensional reduction on thermoelectric performance. Overall, these results demonstrate that reactive SPS is a rapid and efficient method for MAX phase synthesis.
The ever-increasing demand for high-density, energy-efficient data storage, propelled by AI and cloud infrastructures, is driving advancements in heat-assisted magnetic recording (HAMR) media. L10-ordered FePt granular thin films are recognized as leading candidates owing to their exceptional thermal stability and potential for sub-10 nm grain sizes. Here, we investigate FePt-BN granular films, systematically optimizing their microstructure and magnetic properties by tuning process parameters including BN atomic fraction of FePt-BN targets, N2 gas flow, and film thickness. Employing a hybrid workflow that integrates conventional trial-and-error experimentation with machine learning such as principle component analysis, random forest regression and Bayesian optimization we identify key determinants governing average main- and sub- grain size (D1, D2), coercivity (μ0Hc), and chemical ordering. Machine-learning analysis reveals that the degree of L10 order is the primary factor for μ0Hc, while D1 is mainly influenced by BN content and microstructural attributes derived from transmission electron microscopy data. Active-learning-guided Bayesian optimization enabled us to rapidly achieve FePt-BN media with high μ0Hc (2.4 T), sub 6 nm grain diameters, and areal grain densities exceeding 12 Tgrains /in2 outperforming traditional heuristic approaches with fewer experiments. Our results underscore the power of data-driven process optimization for accelerated HAMR materials development, enabling the realization of next-generation hard disk drives with ultra-high recording densities.
