Search for: All records

Abstract Direct disposal of used soft electronics into the environment can cause severe pollution to the ecosystem due to the inability of most inorganic materials and synthetic polymers to biodegrade. Additionally, the loss of the noble metals that are commonly used in soft electronics leads to a waste of scarce resources. Thus, there is an urgent need to develop “green” and sustainable soft electronics based on eco‐friendly manufacturing that may be recycled or biodegraded after the devices’ end of life. Here an approach to fabricating sustainable soft electronics is demonstrated where the expensive functional materials can be recycled and the soft substrate can be biodegradable. A stretchable agarose/glycerol gel film is used as the substrate, and silver nanowires (AgNWs) are printed on the film to fabricate the soft electronic circuits. The mechanical and chemical properties of the agarose/glycerol gel films are characterized, and the functionality of the printed AgNW electrodes for electrophysiological sensors is demonstrated. The demonstration of the biodegradability of the agarose/glycerol and the recyclability of AgNWs points toward ways to develop sustainable and eco‐friendly soft electronics. 

An approach to designing multiconfiguration afocal telescopes is developed and demonstrated. Freeform surfaces are used to maximize the achievable diffraction-limited zoom ratio while staying in a compact volume for a two-position multiconfiguration afocal optical system. The limitations of these systems with three-mirror beam paths are discussed and subsequently overcome by introducing an additional degree of freedom. In a four-mirror beam path system, the goal of a 5x zoom ratio is achieved with a compensated exit pupil and diffraction-limited performance. A significant benefit in optical performance when using freeform surfaces is shown compared to more conventional surface types. 

Abstract Despite decades of progress, developing minimally invasive bone‐specific drug delivery systems (DDS) to improve fracture healing remains a significant clinical challenge. To address this critical therapeutic need, nanoparticle (NP) DDS comprised of poly(styrene‐alt‐maleic anhydride)‐b‐poly(styrene) (PSMA‐b‐PS) functionalized with a peptide that targets tartrate‐resistant acid phosphatase (TRAP) and achieves preferential fracture accumulation has been developed. The delivery of AR28, a glycogen synthase kinase‐3 beta (GSK3β) inhibitor, via the TRAP binding peptide‐NP (TBP‐NP) expedites fracture healing. Interestingly, however, NPs are predominantly taken up by fracture‐associated macrophages rather than cells typically associated with fracture healing. Therefore, the underlying mechanism of healing via TBP‐NP is comprehensively investigated herein. TBP‐NP_AR28promotes M2 macrophage polarization and enhances osteogenesis in preosteoblast‐macrophage co‐cultures in vitro. Longitudinal analysis of TBP‐NP_AR28‐mediated fracture healing reveals distinct spatial distributions of M2 macrophages, an increased M2/M1 ratio, and upregulation of anti‐inflammatory and downregulated pro‐inflammatory genes compared to controls. This work demonstrates the underlying therapeutic mechanism of bone‐targeted NP DDS, which leverages macrophages as druggable targets and modulates M2 macrophage polarization to enhance fracture healing, highlighting the therapeutic benefit of this approach for fractures and bone‐associated diseases. 

Particle dynamics and multi-agent systems provide accurate dynamical models for studying and forecasting the behaviour of complex interacting systems. They often take the form of a high-dimensional system of differential equations parameterized by an interaction kernel that models the underlying attractive or repulsive forces between agents. We consider the problem of constructing a data-based approximation of the interacting forces directly from noisy observations of the paths of the agents in time. The learned interaction kernels are then used to predict the agents’ behaviour over a longer time interval. The approximation developed in this work uses a randomized feature algorithm and a sparse randomized feature approach. Sparsity-promoting regression provides a mechanism for pruning the randomly generated features which was observed to be beneficial when one has limited data, in particular, leading to less overfitting than other approaches. In addition, imposing sparsity reduces the kernel evaluation cost which significantly lowers the simulation cost for forecasting the multi-agent systems. Our method is applied to various examples, including first-order systems with homogeneous and heterogeneous interactions, second-order homogeneous systems, and a new sheep swarming system. 

Computational design of functional proteins is of both fundamental and applied interest. This study introduces a generative framework for co‐designing protein sequence and structure in a unified process by modeling their joint distribution, with the goal of enabling cross‐modality interactions toward coherent and functional designs. Each residue is represented by three distinct modalities (type, position, and orientation) and modeled using dedicated diffusion processes: multinomial for types, Cartesian for positions, and special orthogonal group SO(3) for orientations. To couple these modalities, we propose a unified architecture, ReverseNet, which employs a shared graph attention encoder to integrate multimodal information and separate projectors to predict each modality. We benchmark our models, JointDiff and JointDiff‐x, on unconditional monomer design and conditional motif scaffolding tasks. Compared to two‐stage design models that generate sequence and structure separately, our models produce monomer structures with comparable or better designability, while currently lagging in sequence quality and motif scaffolding performance based on computational metrics. However, they are 1–2 orders of magnitude faster and support rapid iterative improvements through classifier‐guided sampling. To complement computational evaluations, we experimentally validate our approach through a case study on green fluorescent protein (GFP) design. Several novel, evolutionarily distant variants generated by our models exhibit measurable fluorescence, confirming functional activity. These results demonstrate the feasibility of joint sequence–structure generation and establish a foundation to accelerate functional protein design in future applications. Codes, data, and trained models are accessible athttps://github.com/Shen-Lab/JointDiff. 

Screen printing is a promising route towards high throughput printed electronics. Currently, the preparation of nanomaterial based conductive inks involves complex formulations with often toxic surfactants in the ink's composition, making them unsuitable as an eco-friendly printing technology. This work reports the development of a silver nanowire (AgNW) ink with a relatively low conductive particle loading of 7 wt%. The AgNW ink involves simple formulation and comprises a biodegradable binder and a green solvent with no toxic surfactants in the ink formulation, making it an eco-friendly printing process. The formulated ink is suitable for printing on a diverse range of substrates such as polydimethylsiloxane (PDMS), polyethylene terephthalate (PET), polyimide (PI) tape, glass, and textiles. By tailoring the rheological behaviour of the ink and developing a one-step post-printing process, a minimum feature size of 50 μm and conductivity as high as 6.70 × 10 6 S m −1 was achieved. Use of a lower annealing temperature of 150 °C makes the process suitable for plastic substrates. A flexible textile heater and a wearable hydration sensor were fabricated using the reported AgNW ink to demonstrate its potential for wearable electronic applications.