Ex Vivo Evaluation of Combination Immunotherapy Using Tumor-Microenvironment-on-Chip.

Combination immunotherapy has emerged as a promising strategy to address the challenges associated with immune checkpoint inhibitor (ICI) therapy in breast cancer. The efficacy of combination immunotherapy hinges upon the intricate and dynamic nature of the tumor microenvironment (TME), characterized by cellular heterogeneity and molecular gradients. However, current methodologies for drug screening often fail to accurately replicate these complex conditions, resulting in limited predictive capacity for treatment outcomes. Here, a tumor-microenvironment-on-chip (TMoC), integrating a circulation system and ex vivo tissue culture with physiological oxygen and nutrient gradients, is described. This platform enables spatial infiltration of cytotoxic CD8+ T cells and their targeted attack on the tumor, while preserving the high complexity and heterogeneity of the TME. The TMoC is employed to assess the synergistic effect of five targeted therapy drugs and five chemotherapy drugs in combination with immunotherapy, demonstrating strong concordance between chip and animal model responses. The TMoC holds significant potential for advancing drug development and guiding clinical decision-making, as it offers valuable insights into the complex dynamics of the TME.

ε-Ga2 O3 : An Emerging Wide Bandgap Piezoelectric Semiconductor for Application in Radio Frequency Resonators.

The explosion of mobile data from the internet of things (IoT) is leading to the emergence of 5G technology with dramatic frequency band expansion and efficient band allocations. Along with this, the demand for high-performance filters for 5G radio frequency (RF) front-ends keeps growing. The most popular 5G filters are constructed by piezoelectric resonators based on AlN semiconductor. However, AlN possesses a piezoelectric constant d33 lower than 5 pm V-1 and it becomes necessary to develop novel semiconductors with larger piezoelectric constant. In this work, it is shown that strong piezoelectricity exists in ε-Ga2 O3 . High-quality phase-pure ε-Ga2 O3 thin films with a relatively low residual stress are prepared. A switching spectroscopy piezoelectric force microscope (SS-PFM) measurement is carried out and the piezoelectric constant d33 of ε-Ga2 O3 is determined to be ≈10.8-11.2 pm V-1 , which is twice as large as that of AlN. For the first time, surface acoustic wave (SAW) resonators are demonstrated on the ε-Ga2 O3 thin films and different vibration modes resonating in the GHz range are observed. The results suggest that ε-Ga2 O3 is a great material candidate for application in piezoelectric devices, thanks to its wide bandgap, strong piezoelectric property, small acoustic impedance, and low residual stress.

Ferroelectric photosensor network: an advanced hardware solution to real-time machine vision.

Nowadays the development of machine vision is oriented toward real-time applications such as autonomous driving. This demands a hardware solution with low latency, high energy efficiency, and good reliability. Here, we demonstrate a robust and self-powered in-sensor computing paradigm with a ferroelectric photosensor network (FE-PS-NET). The FE-PS-NET, constituted by ferroelectric photosensors (FE-PSs) with tunable photoresponsivities, is capable of simultaneously capturing and processing images. In each FE-PS, self-powered photovoltaic responses, modulated by remanent polarization of an epitaxial ferroelectric Pb(Zr0.2Ti0.8)O3 layer, show not only multiple nonvolatile levels but also sign reversibility, enabling the representation of a signed weight in a single device and hence reducing the hardware overhead for network construction. With multiple FE-PSs wired together, the FE-PS-NET acts on its own as an artificial neural network. In situ multiply-accumulate operation between an input image and a stored photoresponsivity matrix is demonstrated in the FE-PS-NET. Moreover, the FE-PS-NET is faultlessly competent for real-time image processing functionalities, including binary classification between 'X' and 'T' patterns with 100% accuracy and edge detection for an arrow sign with an F-Measure of 1 (under 365 nm ultraviolet light). This study highlights the great potential of ferroelectric photovoltaics as the hardware basis of real-time machine vision.

Highly Controllable and Silicon-Compatible Ferroelectric Photovoltaic Synapses for Neuromorphic Computing.

Ferroelectric synapses using polarization switching (a purely electronic switching process) to induce analog conductance change have attracted considerable interest. Here, we propose ferroelectric photovoltaic (FePV) synapses that use polarization-controlled photocurrent as the readout and thus have no limitations on the forms and thicknesses of the constituent ferroelectric and electrode materials. This not only makes FePV synapses easy to fabricate but also reduces the depolarization effect and hence enhances the polarization controllability. As a proof-of-concept implementation, a Pt/Pb(Zr0.2Ti0.8)O3/LaNiO3 FePV synapse is facilely grown on a silicon substrate, which demonstrates continuous photovoltaic response modulation with good controllability (small nonlinearity and write noise) enabled by gradual polarization switching. Using photovoltaic response as synaptic weight, this device exhibits versatile synaptic functions including long-term potentiation/depression and spike-timing-dependent plasticity. A simulated FePV synapse-based neural network achieves high accuracies (>93%) for image recognition. This study paves a new way toward highly controllable and silicon-compatible synapses for neuromorphic computing.