The electrochemical conversion of CO2 to CO in membrane electrode assembly (MEA) electrolyzers using gas diffusion electrodes (GDEs) offers a sustainable and scalable pathway for carbon utilization. Here, we present a one-step atomic layer deposition (ALD) approach to prepare ZnO-based GDEs with tunable loadings and high selectivity toward CO. Increasing the number of ALD cycles raises the ZnO loading but progressively reduces the pore accessibility within the GDE. An optimal balance is achieved at 200 ALD cycles, delivering a peak CO faradaic efficiency (FECO) of 88% and a full-cell energy efficiency of 38% at −100 mA cm−2. Crucially, the scalability of ALD is demonstrated through stable long-term testing, achieving 85% FECO in a 5 cm2 MEA after 30 h, and 80% FECO in a 100 cm2 MEA after 24 h. These results establish ALD as an effective and versatile strategy for fabricating high-performance ZnO electrodes for CO2 electrolysis.
Regenerative peripheral nerve interfaces (RPNIs) are emerging platforms capable to translate neural activity into controllable myoelectric signals. However, current clinical RPNIs rely on muscle autografts, limiting their scalability and extensive adoption. Here, we report a bioactive, ultrasound-responsive piezoelectric muscle biomaterial designed as a fully tissue-engineered alternative to autologous grafts. The construct consists of fibrinogen-based muscle tissues enriched with barium titanate nanoparticles (BTNPs, diameter∼60 nm) and supported by a biodegradable surgical membrane that promotes the formation of aligned, multinucleated myotubes. The incorporation of BTNPs imparts intrinsic piezoelectric activity to the construct, and the nanoparticles are taken up by developing myotubes, enabling remote mechanoelectrical tissue stimulation under low-intensity pulsed ultrasound (LIPUS).In vivo, piezoelectric constructs implanted around the rat peroneal nerve for two months undergo LIPUS-driven activation of internalized BTNPs, which enhances muscle maturation, and electromechanical responsiveness, yielding myoelectrical signals up to 3.2 mV upon nerve activation. Histological analyses confirm improved structural organization, increased desmin expression, and evidence of neovascularization and axonal regeneration within the engineered interface. These findings suggest that the piezoelectric constructs form stable, functional biointerfaces with peripheral nerves, and that LIPUS-driven activation of embedded BTNPs provides a non-invasive strategy to potentiate muscle development and signal transduction.This study positions LIPUS-responsive, engineered piezoelectric muscle constructs as a donor-free, bioactive platform alternative to traditional autografts for next-generation human-machine interfaces and regenerative bioelectronics.
Over the past four decades, Dr. Rakesh K. Jain has fundamentally reshaped the conceptual framework of solid tumor biology by establishing tumor pathophysiology as a transport- and mechanics-governed system. Through pioneering intravital microscopy studies, his work revealed how abnormal vasculature, elevated interstitial fluid pressure, solid stress, and microenvironmental heterogeneity collectively regulate drug delivery, immune infiltration, and therapeutic response. These mechanistic insights transformed cancer from a purely cellular disease into a complex biophysical and multicellular ecosystem. In parallel with these conceptual advances, bioengineering technologies have evolved to reconstruct and interrogate tumor pathophysiology in controlled in vitro settings. Cancer-on-chip (CoC) platforms now integrate three-dimensional (3D) architecture, dynamic perfusion, tunable extracellular matrices, vascular interfaces, and immune components to recapitulate key determinants of tumor progression and treatment resistance. By enabling precise perturbation of mechanical forces, transport barriers, and multicellular interactions, these microphysiological systems provide experimentally accessible “biological twins” of patient tumors. In this Review, we examine how next-generation CoC models translate foundational principles of tumor pathophysiology into engineered platforms for mechanistic investigation and functional precision oncology. We discuss advances in vascularized and immune-competent systems, microenvironment-mediated drug resistance modeling, and the integration of real-time biosensing and spatial omics. Finally, we outline how data generated from these biological twins can inform emerging digital twin frameworks, bridging experimental tumor bioengineering with predictive computational oncology. State-of-the-art CoC systems enable controlled interrogation of microenvironment-driven tumor behaviors, including drug-delivery constraints, immune exclusion, and adaptive resistance, which remain difficult to dissect mechanistically in conventional experimental systems. However, clinical translation remains limited, with existing studies limited to small cohorts and employing heterogeneous methodologies. The lack of standardized endpoints and correlation frameworks remains a major barrier. In this sense, CoC technologies do not depart from tumor physiology, rather, they represent its engineered continuation, extending the mechanistic legacy of intravital tumor biology into human-based, perturbable, and quantitatively interpretable systems. CoC models can capture key features of the native TME and be interrogated for mechanistic studies and drug response evaluation. Through real-time monitoring and comprehensive endpoint analysis, their outputs can be correlated with human clinical outcomes. However, the effective use of these platforms in pre-clinical and clinical research workflows still requires further validation. Increased standardization, together with tighter integration of CoC systems with computational modelling and digital twin approaches, will be essential to fully realize their potential in precision oncology.
In core@shell quantum dots (QDs), the interface between semiconductors of different chemical character largely determines their optoelectronic properties. In III-V@II-VI systems, this boundary involves pronounced chemical and electronic discontinuities that can generate trap states even under complete surface passivation. Using density functional theory on atomistic models of InAs@CdSe QDs, we systematically reconstruct atomic arrangements at the surface and interface to evaluate how local coordination and interfacial dipoles influence the electronic structure. Abrupt interfaces induce charge imbalance and band gap collapse, whereas introducing an alloyed interlayer that mixes core and shell atoms and vacancies restores energetic alignment and yields delocalized band-edge states, consistent with experimental findings. We also introduce a charge-flow analysis that quantifies charge redistribution across the QD, providing a framework for realistic modeling of interlayer formation and predictive design of defect-free interfaces in core@shell architectures.
This paper comprehensively surveys research trends in imitation learning (IL) for contact-rich robotic tasks. Contact-rich tasks, which require complex physical interactions with the environment, represent a central challenge in robotics due to their nonlinear dynamics and sensitivity to small positional deviations. The paper examines demonstration collection methodologies, including teaching methods and sensory modalities crucial for capturing subtle interaction dynamics. We then analyze IL approaches, highlighting their applications to contact-rich manipulation. Recent advances in multimodal learning and foundation models have significantly enhanced performance in complex contact tasks across industrial, household, and healthcare domains. Through systematic organization of current research and identification of challenges, this survey provides a foundation for future advancements in contact-rich robotic manipulation.
