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Recent breakthroughs in brain-inspired computing promise to address a wide range of problems from security to healthcare. However, the current strategy of implementing artificial intelligence algorithms using conventional silicon hardware is leading to unsustainable energy consumption. Neuromorphic hardware based on electronic devices mimicking biological systems is emerging as a low-energy alternative, although further progress requires materials that can mimic biological function while maintaining scalability and speed. As a result of their diverse unique properties, atomically thin two-dimensional (2D) materials are promising building blocks for next-generation electronics including nonvolatile memory, in-memory and neuromorphic computing, and flexible edge-computing systems. Furthermore, 2D materials achieve biorealistic synaptic and neuronal responses that extend beyond conventional logic and memory systems. Here, we provide a comprehensive review of the growth, fabrication, and integration of 2D materials and van der Waals heterojunctions for neuromorphic electronic and optoelectronic devices, circuits, and systems. For each case, the relationship between physical properties and device responses is emphasized followed by a critical comparison of technologies for different applications. We conclude with a forward-looking perspective on the key remaining challenges and opportunities for neuromorphic applications that leverage the fundamental properties of 2D materials and heterojunctions. 

We discuss how a dual-gated memtransistor crossbar can accelerate the extraction of the Transformer’s attention scores. A memtransistor is a novel two-dimensional material-based device that offers non-volatile programmability and gate tunability. Leveraging these attributes, we demonstrate the extraction of quadratic-order products on a single memtransistor and the single-step extraction of attention scores without inferring intermediate query/key vectors. The query/key-free processing of memtransistor-based attention scoring results in 2.37× lower energy with less than half crossbar cells. 

BSTRACT:Piezoelectricmaterialsare used to fabricateacoustictransducersforbubblechambersin searchfor particlesof dark matter.It has been shownthat bubblesinitiatedby nuclearrecoilsemit acousticradiationdistinguishablefrom the phasetransitionscausedby alpha-decay�themain backgroundnoisein such searches.However,these piezoelectricmaterialsmust exhibitultralowradioactivityto minimizethe neutronbackgroundfor dark matterdetectionwhilepossessinghigh acousticsensitivity.Here,for the first time, we demonstrateradiopurehigh-performancepiezoelectricceramicsmeetingthe criteriafor acousticsensing.The screeningofradiopureprecursorsis performedto identifythose with low238U,232Th, and210Pbcontents.Usingthe radiopureprecursors,piezoelectricceramicswith varyingcompositionsare synthesized,and their electromechanicalacousticsensingperformanceis evaluated.Multiplesynthesismodificationssuch as dopingand texturingare utilizedtotailor the piezoelectriccoefficientsof the piezoelectricceramics,and the relationshipbetweenthe piezoelectriccoefficientsand acousticsensingperformanceof the ceramicsis investigated.Acoustictransducersfabricatedusing texturedPb(Mg1/3Nb2/3)O3−PbTiO3(PMN−PT)ceramicsare found to exhibitsuperioracousticsensitivitydue totheir high piezoelectrictransductioncoefficient(d33×g33). This study demonstratesa usefulfigure of merit (FOM)for acousticsensingin bubblechambers 

The growth of layered 2D compounds is a key ingredient in finding new phenomena in quantum materials, optoelectronics, and energy conversion. Here, we report SnP₂Se₆, a van der Waals chiral (R3 space group) semiconductor with an indirect bandgap of 1.36 to 1.41 electron volts. Exfoliated SnP₂Se₆flakes are integrated into high-performance field-effect transistors with electron mobilities >100 cm²/Vs and on/off ratios >10⁶at room temperature. Upon excitation at a wavelength of 515.6 nanometer, SnP₂Se₆phototransistors show high gain (>4 × 10⁴) at low intensity (≈10⁻⁶W/cm²) and fast photoresponse (< 5 microsecond) with concurrent gain of ≈52.9 at high intensity (≈56.6 mW/cm²) at a gate voltage of 60 V across 300-nm-thick SiO₂dielectric layer. The combination of high carrier mobility and the non-centrosymmetric crystal structure results in a strong intrinsic bulk photovoltaic effect; under local excitation at normal incidence at 532 nm, short circuit currents exceed 8 mA/cm²at 20.6 W/cm². 

Haplotype-level allelic characterization facilitates research on the functional, evolutionary and breeding-related features of extremely large and complex plant genomes. We report a 21.7-Gb chromosome-level haplotype-resolved assembly in Pinus densiflora. We found genome rearrangements involving translocations and inversions between chromosomes 1 and 3 of Pinus species and a proliferation of specific long terminal repeat (LTR) retrotransposons (LTR-RTs) in P. densiflora. Evolutionary analyses illustrated that tandem and LTR-RT-mediated duplications led to an increment of transcription factor (TF) genes in P. densiflora. The haplotype sequence comparison showed allelic imbalances, including presence–absence variations of genes (PAV genes) and their functional contributions to flowering and abiotic stress-related traits in P. densiflora. Allele-aware resequencing analysis revealed PAV gene diversity across P. densiflora accessions. Our study provides insights into key mechanisms underlying the evolution of genome structure, LTR-RTs and TFs within the Pinus lineage as well as allelic imbalances and diversity across P. densiflora. 

One major challenge in synthetic biology is the deleterious impacts of cellular stress caused by expression of heterologous pathways, sensors, and circuits. Feedback control and dynamic regulation are broadly proposed strategies to mitigate this cellular stress by optimizing gene expression levels temporally and in response to biological cues. While a variety of approaches for feedback implementation exist, they are often complex and cannot be easily manipulated. Here, we report a strategy that uses RNA transcriptional regulators to integrate additional layers of control over the output of natural and engineered feedback responsive circuits. Called riboregulated switchable feedback promoters (rSFPs), these gene expression cassettes can be modularly activated using multiple mechanisms, from manual induction to autonomous quorum sensing, allowing control over the timing, magnitude, and autonomy of expression. We develop rSFPs in Escherichia coli to regulate multiple feedback networks and apply them to control the output of two metabolic pathways. We envision that rSFPs will become a valuable tool for flexible and dynamic control of gene expression in metabolic engineering, biological therapeutic production, and many other applications. 

Markov Chain Monte Carlo (MCMC) has been the de facto technique for sampling and inference of large graphs such as online social networks. At the heart of MCMC lies the ability to construct an ergodic Markov chain that attains any given stationary distribution \pi, often in the form of random walks or crawling agents on the graph. Most of the works around MCMC, however, presume that the graph is undirected or has reciprocal edges, and become inapplicable when the graph is directed and non-reciprocal. Here we develop a similar framework for directed graphs called Non- Markovian Monte Carlo (NMMC) by establishing a mapping to convert \pi into the quasi-stationary distribution of a carefully constructed transient Markov chain on an extended state space. As applications, we demonstrate how to achieve any given distribution \pi on a directed graph and estimate the eigenvector centrality using a set of non-Markovian, history-dependent random walks on the same graph in a distributed manner.We also provide numerical results on various real-world directed graphs to confirm our theoretical findings, and present several practical enhancements to make our NMMC method ready for practical use inmost directed graphs. To the best of our knowledge, the proposed NMMC framework for directed graphs is the first of its kind, unlocking all the limitations set by the standard MCMC methods for undirected graphs.