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Abstract BackgroundDiagnostic pathology depends on complex, structured reasoning to interpret clinical, histologic, and molecular data. Replicating this cognitive process algorithmically remains a significant challenge. As large language models (LLMs) gain traction in medicine, it is critical to determine whether they have clinical utility by providing reasoning in highly specialized domains such as pathology. MethodsWe evaluated the performance of four reasoning LLMs (OpenAI o1, OpenAI o3-mini, Gemini 2.0 Flash Thinking Experimental, and DeepSeek-R1 671B) on 15 board-style open-ended pathology questions. Responses were independently reviewed by 11 pathologists using a structured framework that assessed language quality (accuracy, relevance, coherence, depth, and conciseness) and seven diagnostic reasoning strategies. Scores were normalized and aggregated for analysis. We also evaluated inter-observer agreement to assess scoring consistency. Model comparisons were conducted using one-way ANOVA and Tukey’s Honestly Significant Difference (HSD) test. ResultsGemini and DeepSeek significantly outperformed OpenAI o1 and OpenAI o3-mini in overall reasoning quality (p < 0.05), particularly in analytical depth and coherence. While all models achieved comparable accuracy, only Gemini and DeepSeek consistently applied expert-like reasoning strategies, including algorithmic, inductive, and Bayesian approaches. Performance varied by reasoning type: models performed best in algorithmic and deductive reasoning and poorest in heuristic and pattern recognition. Inter-observer agreement was highest for Gemini (p < 0.05), indicating greater consistency and interpretability. Models with more in-depth reasoning (Gemini and DeepSeek) were generally less concise. ConclusionAdvanced LLMs such as Gemini and DeepSeek can approximate aspects of expert-level diagnostic reasoning in pathology, particularly in algorithmic and structured approaches. However, limitations persist in contextual reasoning, heuristic decision-making, and consistency across questions. Addressing these gaps, along with trade-offs between depth and conciseness, will be essential for the safe and effective integration of AI tools into clinical pathology workflows. 

Two-dimensional (2D) materials offer immense potential for scientific breakthroughs and technological innovations. While early demonstrations of 2D material-based electronics, optoelectronics, flextronics, straintronics, twistronics, and biomimetic devices exploited micromechanically-exfoliated single crystal flakes, recent years have witnessed steady progress in large-area growth techniques such as physical vapor deposition (PVD), chemical vapor deposition (CVD), and metal–organic CVD (MOCVD). However, use of high growth temperatures, chemically-active growth precursors and promoters, and the need for epitaxy often limit direct growth of 2D materials on the substrates of interest for commercial applications. This has led to the development of a large number of methods for the layer transfer of 2D materials from the growth substrate to the target application substrate with varying degrees of cleanliness, uniformity, and transfer-related damage. This review aims to catalog and discuss these layer transfer methods. In particular, the processes, advantages, and drawbacks of various transfer methods are discussed, as is their applicability to different technological platforms of interest for 2D material implementation.