Semiconductor p–n heterojunctions are widely used in modern electronic and optoelectronic devices, typically generating unipolar photocurrents. By integrating a photoelectrochemical cell (PEC), it becomes possible to achieve more versatile behavior, including bidirectional photocurrents with polarity switching under different applied biases. In this study, we fabricated and optimized PEC photodetectors based on solution-processed p-type CuI thin films combined with n-type 1D TiO₂ nanorods. The optimization was performed by adjusting the number of CuI layers via spin-coating and the applied voltage to enhance the photoresponse. Interestingly, dual-polarity photocurrent was observed in the p-CuI/n-TiO₂ PEC device under visible light illumination (420 nm). Although the 1D n-TiO₂ nanorods are transparent to this blue light and the p-CuI itself typically produces a negative photocurrent, a positive photocurrent was generated under a specific biased voltage. This unique behavior is attributed to the built-in electric field at the p–n junction, which enables bidirectional photocurrent under varying bias conditions. The findings suggest that the device's photocurrent polarity can be effectively tuned by adjusting the applied bias and material configuration. This behavior presents a promising strategy for improving the performance and efficiency of PEC-based photodetectors, offering potential applications in advanced optoelectronic devices for energy conversion, light sensing, and future nanotechnology-based applications in renewable energy systems.

In this study, we present a groundbreaking approach to designing high-efficiency solar cells by combining nanophotonics with machine learning (ML). Our objective is to develop a plasmonic-enhanced solar cell that incorporates 2D material heterostructures such as MoS₂ and graphene. These advanced materials can be coupled with silver (Ag) nanoparticle arrays to boost light absorption through localized surface plasmon resonance (LSPR). To fine-tune the design, we employed AI-assisted simulations that predict the optimal configuration of nanoparticle size, inter-particle spacing, and the thickness of the 2D material layers.

The simulation process is based on finite-difference time-domain (FDTD) modeling, implemented in Python using the pyFDTD library. This approach allowed us to simulate and analyze how light interacts with the plasmon-enhanced solar cell structure. We then trained a neural network using TensorFlow, drawing from a dataset of over 1,000 simulation results. This model predicts the maximum light absorption efficiency for varying design parameters, including nanoparticle radius, particle spacing, and the wavelength of incoming light.

Through this AI-guided process, we discovered that the ideal nanoparticle size is 42 nm with a spacing of 55 nm, which maximizes absorption efficiency in the visible light spectrum. The final design demonstrated an exceptional light absorption efficiency of 92.7% across the 400–700 nm wavelength range, surpassing traditional solar cell performance by more than 30%. Furthermore, integrating machine learning reduced simulation times by approximately 80%, offering a highly efficient and scalable solution for advancing solar energy technology.

Two-dimensional materials, with their atomic-scale thickness and novel physical properties, represent promising candidates for designing highly integrated artificial intelligence chips. However, their atomic thickness, while advantageous for integration, results in weak light absorption, resulting in a limited photoresponse and hindering their use in artificial vision devices. To address this issue, we propose an optical synaptic device based on an R6G/InSe hybrid structure. In this hybrid device, R6G serves as a photosensitive layer, effectively enhancing the photoresponse of the 2D InSe sensing layer, with this effect attributed to charge transfer between the dye molecules and the channel material. We employed a non-destructive spectroscopic technique, surface-enhanced Raman spectroscopy (SERS), in conjunction with electrical testing, to demonstrate the charge transfer mechanism. Furthermore, we introduced an oxide layer on the InSe surface through oxygen plasma treatment. The presence of the oxide layer hindered the charge transfer process, providing further evidence for the charge transfer mechanism. The proposed hybrid-structure device exhibits outstanding optical synaptic performance, facilitating image denoising preprocessing similar to that achieved by artificial neural network functions. We realized encryption and decryption of image information using artificially introduced noise backgrounds and further proposed a design concept for an anti-counterfeiting chip by designing a hybrid device array. In summary, the design of the R6G/InSe hybrid-structure device we propose offers a reference solution for enhancing the optical response of 2D material devices. Additionally, our encryption and anti-counterfeiting applications present new directions for the use of 2D materials in optoelectronic devices.

Repositioning amiodarone, an antiarrhythmic agent, for cancer therapy has garnered increasing interest due to its inhibitory effects on carnitine palmitoyltransferase 1A (CPT1A), a key enzyme upregulated in ovarian cancer (OC)¹. OC is particularly aggressive and prone to peritoneal metastasis, with a metabolic profile characterized by elevated fatty acid oxidation driven by CPT1A overexpression. Despite its therapeutic potential, the clinical application of amiodarone in oncology is hindered by its systemic toxicity. To address this limitation, drug delivery systems (DDSs) offer a promising strategy, with liposomes emerging as an ideal candidate due to their high biocompatibility and tumor-targeting capabilities. Liposomes can exploit the Enhanced Permeability and Retention (EPR) effect or be surface-modified to enhance receptor-mediated targeting, thereby improving drug accumulation and pharmacokinetics². The production method of liposomes critically influences their physicochemical properties, with conventional techniques often requiring post-processing steps to achieve clinical-grade formulations. Microfluidics has recently gained attention as a scalable and precise alternative, offering fine control over parameters such as vesicle size, lamellarity, and distribution³. Using a microfluidic approach, amiodarone was successfully encapsulated into liposomes, with simultaneous generation of amiodarone-containing lipidic particles. Optimization of process parameters, including temperature and flow rate ratio, enabled the production of a stable, uniform DDS. Both formulations were extensively characterized and demonstrated significant efficacy in in vitro models of ovarian cancer, underscoring their potential for further development in targeted cancer therapy⁴.

[1] Sawyer B.T., Qamar L., Yamamoto T.M., McMellen A., Watson Z. L., Richer, J. K., Behbakht K., Schlaepfer I.R., Bitler B. G. 2020, Molecular Cancer Research, 18, 1088.

[2] Bitounis D., Fanciullino R., Iliadis A., Ciccolini J. 2012, ISRN Pharmaceutics, 2012, 1, 11.

[3] Carugo D., Bottaro E., Owen J., Stride E., Nastruzzi, C. 2016, Scientific Reports, 6, 25876.

[4] Saorin A., Saorin G., Duzagac F., Parisse P., Cao N., Corona G., Cavarzerani E., Rizzolio F. 2024, Scientific Reports, 14, 6280.

Thermoset polymers are the most common matrix used for manufacturing fiber-reinforced polymer composites, with their exceptional properties leading to their usage in highly demanding fields such as aerospace. Monitoring such materials for structural deformation is key when it comes to maintaining their continual usage and avoiding catastrophic failure. Here, we explore how single-walled carbon nanotube (SWCNTs) nano–macro arrangements can be used to monitor composites under tensile loading without causing a loss in host material properties. Sensors of various thicknesses (23, 37, and 53 nm) were investigated to determine optimized monitoring parameters: electrode materials, configuration, and embedding within or surface application. Tensile loading was conducted with a crosshead speed of 1mm/min, and strain measurements were obtained using digital image correlation. It was found that thinner sensors provided higher sensitivity to mechanical deformation and load, and that embedded sensors outperform surface-applied ones (gauge factors between 23 and 86 and 4 and 10, respectively). All sensors were shown to accurately measure strain, and were shown to self-adjust to elastic and plastic deformation regimes during mechanical loading. We show that monitoring of the mechanical deformation and loading of popular industrial polymers and composites can be accurately conducted using optimized SWCNT structures, and that their application causes no degradation in thermoset polymer material performance.

Glioblastoma multiforme (GBM) is one of the most aggressive and treatment-resistant forms of brain cancer, characterized by rapid progression, high recurrence, and a limited response to conventional therapies. This clinical challenge highlights the urgent need for advanced, multimodal therapeutic strategies. In this study, we present a dual-responsive nanoplatform engineered from iron oxide (Fe₃O₄) nanoparticles and gold nanorods, specifically designed to facilitate synchronized magnetic hyperthermia (MHT) and photothermal therapy (PTT) in combination with chemotherapeutic delivery and in vivo imaging. The nanohybrid was synthesized through a seed-mediated thermal decomposition approach, yielding uniform, stable, and biocompatible structures with high magnetic responsiveness and strong near-infrared absorption. Upon external application of an alternating magnetic field and NIR irradiation, the system generated localized heat sufficient to induce therapeutic hyperthermia. In vitro experiments using U87-MG glioblastoma cells demonstrated significant cytotoxicity under combined MHT and PTT, with this combination clearly outperforming either modality alone. In vivo studies in a murine GBM model showed efficient tumor accumulation, pronounced tumor regression, and no detectable systemic toxicity. For added functionality, doxorubicin was conjugated for chemotherapeutic synergy, and a near-infrared fluorescent dye was co-loaded to facilitate real-time biodistribution analysis and image-guided therapy. This multifunctional nanoplatform represents a promising minimally invasive strategy for effective glioblastoma treatment through thermally synchronized, targeted intervention.

The assembly of nanoparticles into mesoscopic structures, known as superparticles (SPs), leads to new emergent properties arising from interactions among their components [1, 2]. Indeed, various types of nanoparticles, ranging from metal chalcogenide and perovskite quantum dots (QDs) to metal nanoparticles and magnetite nanocubes, can act as functional building blocks for artificial solids displaying unique properties that transcend those of their constituents. Within this landscape, SPs based on plasmonic metal nanoparticles [3-5] have attracted significant interest owing to the unique coupling effects between the plasmonic fields of the constituent nanoparticles, holding great promise for several applications, including ultra-efficient surface-enhanced Raman scattering [6,7]. Despite this promising outlook, the understanding of the fundamental factors driving the behaviour of metal SPs is still incomplete.

Here, we assembled gold nanoparticles (AuNPs) into 200 SPs with varying interparticle distances. After performing a structural characterisation of the resulting SPs by TEM, DLS, and SAXS, we leveraged transient absorption spectroscopy (TA) and transient absorption microscopy (μPP) to unravel their ultrafast photophysics. Our results shed light on the role of interactions between plasmon resonances in determining the overall optical response of metal SPs. In fact, both spectral shape and kinetics show a dependence upon the interparticle distance, revealing the emergence of a collective response of the SP to photoexcitation due to interactions between the constituent nanoparticles. The results pave the way to the engineering of functional metal-based superstructures for a variety of possible applications in photonics and optoelectronics.

[1] ACS Nano 2020, 14, 10, 13806–13815.

[2] Nat Synth, 2023, 10.1038/s44160-023-00407-2.

[3] Chem. Rev. 2020, 120, 2, 464–525.

[4] Nanoscale Adv., 2020, 2, 3764-3787.

[5] Nat. Comm, 11, 2771, 2020.

[6] Adv. Funct. Mater. 2020, 30, 2005400.

[7] Nanoscale, 2019, 11, 17444-17459.

As a major energy consumer in modern society, the promotion of green and energy-efficient buildings, along with the enhancement of energy efficiency in existing structures, has become an urgent priority. Windows, as critical components for lighting and thermal exchange in buildings, play a pivotal role in energy conservation through optimized design. Traditional energy-saving windows, such as double-glazed or Low-e glass, offer certain energy-saving benefits but lack the capability for intelligent adjustment to varying environmental conditions. Smart windows, particularly electrochromic (EC) and photochromic (PC) smart windows, provide a promising solution for dynamically regulating light and heat exchange. EC windows exhibit superior dynamic regulation capabilities but require electrical support, while PC windows operate without external energy sources but are limited in response speed and modulation range. This study presents a novel all-solid-state electro-photo dual-responsive smart window based on semiconductor-coupled heterojunction composed of ZnO nanoparticles and oxygen-deficient WO_3-x, which not only automatically adjusts transmittance in response to light intensity (ΔT^PC = 41.2%) but also enables active regulation its transmittance through applied electrical field (ΔT^EC = 61.9%). By leveraging EC and PC functionalities, the window achieves significant temperature modulation of 5.3°C and 4.7°C, respectively, demonstrating exceptional thermal regulation performance and energy-saving potential. This innovative technology offers a new direction for the development and application of high-performance smart windows, paving the way for a more energy-efficient, environmentally sustainable future in the construction industry.

This study investigates the influence of carbon nanotube sponges (CNT-sponges) at concentrations of 0.1, 0.3, and 0.5 wt% on the rheological behavior of three series of poly(styrene-co-acrylonitrile) [P(S:AN)] polymer solutions. Amplitude and frequency sweep tests were performed to evaluate the Linear Viscoelastic Range (LVER), storage modulus (G'), loss modulus (G''), loss factor (tan δ), structural behavior, and overall homogeneity of the composite solutions. The rheological measurements revealed a viscosity range between 0.8 and 20 Pa·s across all samples. The loss factor analysis indicated a liquid-like viscoelastic response for solutions containing 0.1 and 0.3 wt% CNT-sponges, while the 0.5 wt% formulation exhibited a solid-like response, suggesting increased elasticity and network formation at higher CNT concentrations.

These rheological insights are critical for predicting the electrospinnability of the solutions, as viscoelastic behavior significantly influences fiber formation. To validate the suitability of each formulation for electrospinning, all solutions were processed using a standard electrospinning setup. The resulting nanofibers were characterized by scanning electron microscopy (SEM) to assess morphology, fiber continuity, and uniformity.

The findings confirm that rheological evaluation can effectively guide the formulation of electrospinnable composite solutions. Specifically, a CNT content of 0.3 wt% was identified as optimal for achieving balanced flow behavior and fiber quality, highlighting its potential for advanced fiber-based applications.

Nanotechnology plays a crucial role in addressing critical environmental challenges, particularly in the water sector. Among its promising applications, nanotechnology-based tools for water desalination have shown significant potential and innovation. Nanobiotechnology, as an interdisciplinary field, integrates principles from nanotechnology, biotechnology, materials science, physics, and chemistry to manipulate nanometer-scale materials for diverse scientific applications. Titanium dioxide nanoparticles (TiO₂ NPs) can be synthesized through various physical, chemical, and biological (green) methods. Green synthesis, utilizing biological resources, offers an eco-friendly, cost-effective, and safer alternative, demonstrating superior efficiency compared to conventional physical and chemical techniques. TiO₂ nanoparticles find versatile applications across different domains. In the food industry, they serve as photocatalysts and as coating materials in food packaging to enhance product stability and safety. Additionally, TiO₂ nanoparticles are incorporated into electrochemical biosensors to develop advanced nanostructured matrices. Recent research has highlighted the use of natural products from medicinal plants such as Jatropha curcas, Santalum album, Averrhoa carambola, Punica granatum, Beta vulgaris, Ziziphus spina-christi, Syzygium cumini, and Ficus benjamina for the green synthesis of TiO₂ nanoparticles applicable in industrial wastewater treatment. Owing to their unique physicochemical properties—including high photocatalytic activity, chemical stability, cost-effectiveness, and low toxicity—TiO₂ nanoparticles have become one of the most extensively studied nanomaterials for treating industrial effluents. Their efficacy is largely attributed to their capacity to generate reactive oxygen species (ROS) upon irradiation, facilitating the degradation of a broad spectrum of organic and inorganic pollutants. This review further explores the utilization of TiO₂ nanoparticles synthesized via plant extracts for industrial wastewater treatment applications, emphasizing their roles in antimicrobial action, the adsorption of pollutants, and photocatalytic degradation processes mediated by reactive oxygen species.