Microrobots are recognized as transformative solutions for drug delivery systems (DDSs) because they can navigate through the body to specific locations and enable targeted drug release. However, their realization is substantially limited by insufficient payload capacity, unavoidable drug leakage/deactivation, and strict modification/stability criteria for drugs. Natural puffballs possess fascinating features that are highly desirable for DDSs, including a large fruitbody for storing spores, a flexible protective cap, and environmentally triggered release mechanisms. This report presents a puffball‐inspired microrobotic system which incorporates an internal chamber for loading large drug quantities and spatial drug separation, and a near‐infrared‐responsive top‐sealing layer offering strong drug protection and on‐demand release. These puffball‐inspired microrobots (PIMs) display tunable loading capacities up to high concentrations and enhanced drug protection with minimal drug leakage. Upon near‐infrared laser irradiation, on‐demand drug delivery with rapid release efficiency is achieved. The PIMs also demonstrate translational motion velocities, switchable motion modes, and precise locomotion under a rotating magnetic field. This work provides strong proof‐of‐concept for a DDS that combines the superior locomotion capability of microrobots with the unique characteristics of puffballs, thereby illustrating a versatile avenue for development of a new generation of microrobots for targeted drug delivery.

applications, including diagnostics and therapeutics. [1] Compared to traditionally passive nanomedicines, microrobots are active-matter systems composed of actuatable components including magnetic, [2] acoustic, [3] chemical, [4] and/or materials of biological origin. [5] These properties enable microrobots to navigate their environments and to perform highly specific tasks, such as penetrating deep tissues for drug delivery. Importantly, microrobotic systems can be engineered to perform different functions including targeted drug delivery, [6] cell delivery, [7] sensing, [8] and imaging. [9] However, the human body consists of complex microenvironments with varying pH levels, pressures, and size constraints. Designing microrobots with properties that permit navigation through complex environments like the body while maintaining efficacy is a multifaceted challenge.Surface coatings are widely used as a direct approach for loading cargoes onto microrobots. These coatings capitalize on the interface forces between the microrobotic devices and the loaded cargoes and can be released in response to exogenous or endogenous stimuli. For instance, hydrogen bonding has been employed to non-covalently attach surface-loaded drug cargoes that can then be released via Microrobots can provide spatiotemporally well-controlled cargo delivery that can improve therapeutic efficiency compared to conventional drug delivery strategies. Robust microfabrication methods to expand the variety of materials or cargoes that can be incorporated into microrobots can greatly broaden the scope of their functions. However, current surface coating or direct blending techniques used for cargo loading result in inefficient loading and poor cargo protection during transportation, which leads to cargo waste, degradation and non-specific release. Herein, a versatile platform to fabricate fillable microrobots using microfluidic loading and dip sealing (MLDS) is presented. MLDS enables the encapsulation of different types of cargoes within hollow microrobots and protection of cargo integrity. The technique is supported by highresolution 3D printing with an integrated microfluidic loading system, which realizes a highly precise loading process and improves cargo loading capacity. A corresponding dip sealing strategy is developed to encase and protect the loaded cargo whilst maintaining the geometric and structural integrity of the loaded microrobots. This dip sealing technique is suitable for different materials, including thermal and light-responsive materials. The MLDS platform provides new opportunities for microrobotic systems in targeted drug delivery, environmental sensing, and chemically powered micromotor applications.

Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python.

Abstract3D organoids have been widely used as tractable in vitro models capable of elucidating aspects of human development and disease. However, the manual and low throughput culture methods coupled with a low reproducibility and geometric heterogeneity restricts the scope and application of organoid research. Combining expertise from stem cell biology and bioengineering offers a promising approach to address some of these limitations. Here, we use melt electrospinning writing to generate tuneable grid scaffolds that can guide the self‐organization of pluripotent stem cells into patterned arrays of embryoid bodies. We show that grid geometry is a key determinant of stem cell self‐organization, guiding the position and size of emerging lumens via curvature‐controlled tissue growth. We report two distinct methods for culturing scaffold‐grown embryoid bodies into either interconnected or spatially discrete cerebral organoids. These scaffolds provide a high‐throughput method to generate, culture and analyse large numbers of organoids, substantially reducing the time investment and manual labour involved in conventional methods of organoid culture. We anticipate that this methodological development will open up new opportunities for guiding pluripotent stem cell culture, studying lumenogenesis, and generating large numbers of uniform organoids for high throughput screening.This article is protected by copyright. All rights reserved

Raman spectroscopy is a non-destructive and label-free chemical analysis technique, which plays a key role in the analysis and discovery cycle of various branches of science. Nonetheless, progress in Raman spectroscopic analysis is still impeded by the lack of software, methodological and data standardisation, and the ensuing fragmentation and lack of reproducibility of analysis workflows thereof. To address these issues, we introduce RamanSPy, an open-source Python package for Raman spectroscopic research and analysis. RamanSPy provides a comprehensive library of ready-to-use tools for spectroscopic analysis, which streamlines day-to-day tasks, integrative analyses, as well as novel research and algorithmic development. RamanSPy is modular and open source, not tied to a particular technology or data format, and can be readily interfaced with the burgeoning ecosystem for data science, statistical analysis and machine learning in Python.