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Abstract Nature provides many examples of the benefits of nanoscopic surface structures in areas of adhesion and antifouling. Herein, the design, fabrication, and characterization of liquid crystal elastomer (LCE) films are presented with nanowire surface structures that exhibit tunable stimuli‐responsive deformations and enhanced adhesion properties. The LCE films are shown to curl toward the side with the nanowires when stimulated by heat or organic solvent vapors. In contrast, when a droplet of the same solvent is placed on the film, it curls away from the nanowire side due to nanowire‐induced capillary forces that cause unequal swelling. This characteristic curling deformation is shown to be reversible and can be optimized to match curved substrates, maximizing adhesive shear forces. By using chemical modification, the LCE nanowire films can be given underwater superoleophobicity, enabling oil repellency under a range of harsh conditions. This is combined with the nanowire‐induced frictional asymmetry and the reversible shape deformation to create an underwater droplet mixing robot, capable of performing chemical reactions in aqueous environments. These findings demonstrate the potential of nanowire‐augmented LCE films for advanced applications in soft robotics, adaptive adhesion, and easy chemical modification, with implications for designing responsive materials that integrate mechanical flexibility with surface functionality. 

Designers use third-party intellectual property (IP) cores and outsource various steps in the integrated circuit (IC) design and manufacturing flow. As a result, security vulnerabilities have been rising. This is forcing IC designers and end users to re-evaluate their trust in ICs. If attackers get hold of an unprotected IC, they can reverse engineer the IC and pirate the IP. Similarly, if attackers get hold of a design, they can insert malicious circuits or take advantage of “backdoors” in a design. Unintended design bugs can also result in security weaknesses. This tutorial paper provides an introduction to the domain of hardware security through two pedagogical examples of hardware security problems. The first is a walk-through of the scan chain-based side channel attack. The second is a walk-through of logic locking of digital designs. The tutorial material is accompanied by open access digital resources that are linked in this article. 

Oracle-less machine learning (ML) attacks have broken various logic locking schemes. Regular synthesis, which is tailored for area-power-delay optimization, yields netlists where key-gate localities are vulnerable to learning. Thus, we call for security-aware logic synthesis. We propose ALMOST, a framework for adversarial learning to mitigate oracle-less ML attacks via synthesis tuning. ALMOST uses a simulated-annealing-based synthesis recipe generator, employing adversarially trained models that can predict state-of-the-art attacks’ accuracies over wide ranges of recipes and key-gate localities. Experiments on ISCAS benchmarks confirm the attacks’ accuracies drops to around 50% for ALMOST-synthesized circuits, all while not undermining design optimization. 

Abstract Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacteriumShewanella oneidensisthat enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) fromS. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements. 

Automating hardware design could obviate a signif-icant amount of human error from the engineering process and lead to fewer errors. Verilog is a popular hardware description language to model and design digital systems, thus generating Verilog code is a critical first step. Emerging large language models (LLMs) are able to write high-quality code in other programming languages. In this paper, we characterize the ability of LLMs to generate useful Verilog. For this, we fine-tune pre-trained LLMs on Verilog datasets collected from GitHub and Verilog textbooks. We construct an evaluation framework comprising test-benches for functional analysis and a flow to test the syntax of Verilog code generated in response to problems of varying difficulty. Our findings show that across our problem scenarios, the fine-tuning results in LLMs more capable of producing syntactically correct code (25.9% overall). Further, when analyzing functional correctness, a fine-tuned open-source CodeGen LLM can outperform the state-of-the-art commercial Codex LLM (6.5% overall). We release our training/evaluation scripts and LLM checkpoints as open source contributions. 

A seventh blind test of crystal structure prediction was organized by the Cambridge Crystallographic Data Centre featuring seven target systems of varying complexity: a silicon and iodine-containing molecule, a copper coordination complex, a near-rigid molecule, a cocrystal, a polymorphic small agrochemical, a highly flexible polymorphic drug candidate, and a polymorphic morpholine salt. In this first of two parts focusing on structure generation methods, many crystal structure prediction (CSP) methods performed well for the small but flexible agrochemical compound, successfully reproducing the experimentally observed crystal structures, while few groups were successful for the systems of higher complexity. A powder X-ray diffraction (PXRD) assisted exercise demonstrated the use of CSP in successfully determining a crystal structure from a low-quality PXRD pattern. The use of CSP in the prediction of likely cocrystal stoichiometry was also explored, demonstrating multiple possible approaches. Crystallographic disorder emerged as an important theme throughout the test as both a challenge for analysis and a major achievement where two groups blindly predicted the existence of disorder for the first time. Additionally, large-scale comparisons of the sets of predicted crystal structures also showed that some methods yield sets that largely contain the same crystal structures.