Two-dimensional transition metal dichalcogenides (TMDs) emerged as a promising platform to construct sensitive biosensors. We report an ultrasensitive electrochemical dopamine sensor based on manganese-doped MoS2 synthesized via a scalable two-step approach (with Mn ~2.15 atomic %). Selective dopamine detection is achieved with a detection limit of 50 pM in buffer solution, 5 nM in 10% serum, and 50 nM in artificial sweat. Density functional theory calculations and scanning transmission electron microscopy show that two types of Mn defects are dominant: Mn on top of a Mo atom (MntopMo) and Mn substituting a Mo atom (MnMo). At low dopamine concentrations, physisorption on MnMo dominates. At higher concentrations, dopamine chemisorbs on MntopMo, which is consistent with calculations of the dopamine binding energy (2.91 eV for MntopMo versus 0.65 eV for MnMo). Our results demonstrate that metal-doped layered materials, such as TMDs, constitute an emergent platform to construct ultrasensitive and tunable biosensors.

Doping lies at the heart of modern semiconductor technologies. Therefore, for two-dimensional (2D) semiconducting transition metal dichalcogenides (TMDs), the significance of controlled doping is no exception. Recent studies have indicated that, by substitutionally doping 2D TMDs with a judicious selection of dopants, their electrical, optical, magnetic, and catalytic properties can be effectively tuned, endowing them with great potential for various practical applications. Herein, and inspired by the sol–gel process, we report a liquid-phase precursor-assisted approach for in situ substitutional doping of monolayered TMDs and their in-plane heterostructures with tunable doping concentration. This highly reproducible route is based on the high-temperature chalcogenation of spin-coated aqueous solutions containing host and dopant precursors. The precursors are mixed homogeneously at the atomic level in the liquid phase prior to the synthesis process, thus allowing for an improved doping uniformity and controllability. We further demonstrate the incorporation of various transition metal atoms, such as iron (Fe), rhenium (Re), and vanadium (V), into the lattice of TMD monolayers to form Fe-doped WS2, Re-doped MoS2, and more complex material systems such as V-doped in-plane W x Mo1–x S2–Mo x W1–x S2 heterostructures, among others. We envisage that our developed approach is universal and could be extended to incorporate a variety of other elements into 2D TMDs and create in-plane heterointerfaces in a single step, which may enable applications such as electronics and spintronics at the 2D limit.

Atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDs) can be easily synthesized on SiO2/Si substrates by chemical vapor deposition (CVD). However, for practical applications, those 2D crystals usually need to be retrieved and placed onto target substrates. Hence, a robust and effective transfer process is required. Currently, the most widely used approach for transferring CVD-grown TMDs involves the spin-coating of a poly­(methyl methacrylate) (PMMA) support layer, followed by the wet etching of the SiO2 layer in hot NaOH. This transfer process often causes substantial accumulation of polymer residues as well as severe structural damage of TMDs induced during the etching of substrates at elevated temperatures. In this work, we present an alternative approach for the transfer of CVD-grown TMDs that can address the issues mentioned above. In this process, we replaced PMMA with cellulose acetate (CA) as a support layer and used buffered oxide etch (BOE) as an effective room-temperature etchant for SiO2. The CA-transferred TMDs exhibit well-preserved structural integrity and unaltered optical properties as well as largely improved microscale and nanoscale cleanliness with reduced wrinkles and cracks. Furthermore, we integrated our CA-transfer method with a deterministic positioning system that allowed microprecision transfer of the TMD layers. For example, a WS2–MoS2 vertical heterojunction with an electronically coupled and uniform interface was successfully created. The CA-transfer technique developed in this work represents a cleaner alternative to the PMMA-transfer method, thus permitting atomic resolution characterizations and the implementation of novel applications of CVD-grown TMDs and their heterostructures.

Strain engineering of graphene takes advantage of one of the most dramatic responses of Dirac electrons enabling their manipulation via strain-induced pseudo-magnetic fields. Numerous theoretically proposed devices, such as resonant cavities and valley filters, as well as novel phenomena, such as snake states, could potentially be enabled via this effect. These proposals, however, require strong, spatially oscillating magnetic fields while to date only the generation and effects of pseudo-gauge fields which vary at a length scale much larger than the magnetic length have been reported. Here we create a periodic pseudo-gauge field profile using periodic strain that varies at the length scale comparable to the magnetic length and study its effects on Dirac electrons. A periodic strain profile is achieved by pulling on graphene with extreme (>10%) strain and forming nanoscale ripples, akin to a plastic wrap pulled taut at its edges. Combining scanning tunneling microscopy and atomistic calculations, we find that spatially oscillating strain results in a new quantization different from the familiar Landau quantization observed in previous studies. We also find that graphene ripples are characterized by large variations in carbon-carbon bond length, directly impacting the electronic coupling between atoms, which within a single ripple can be as different as in two different materials. The result is a single graphene sheet that effectively acts as an electronic superlattice. Our results thus also establish a novel approach to synthesize an effective 2D lateral heterostructure -by periodic modulation of lattice strain. Main Text:Due to its high electronic mobility, optical transparency, mechanical strength and flexibility, graphene is attractive for electronic applications 1,2 . However, several factors prevent the realization of common electronic applications. For example, the lack of a band gap prevents an effective off-state in graphene transistors. Furthermore, Klein tunneling 3 , in which electrons pass through an electrostatic barrier with perfect transmission, prevents electron confinement by traditional gating methods. Figure 1. Engineering periodic pseudo electric and magnetic fields at strained interfaces: (a)High(low) density of carbon atoms and hence electrons are created in regions marked by lightblue (yellow) regions due to a strain gradient. This inhomogeneous charge distribution results in an electric field (green arrows). (b) Stretching of bonds cause the Dirac cones at K and K' points to shift symmetrically (yellow) from their original unstrained positions (light-blue) in the reciprocal space. As a momentum shift can be interpreted as generating a pseudo-vector potential term eA/c 15 , (where is the electronic charge and is the velocity of light) this creates pseudo-magnetic fields with opposite signs at the two valleys. (c) The strain associated with rippling creates rare (yellow) and dense (turquoise) regions in the graphene, effectively acting as two different materials in a superlattice. (d) Pseudo...

Graphene provides a unique platform for the detailed study of its dopants at the atomic level. Previously, doped materials including Si, and 0D-1D carbon nanomaterials presented difficulties in the characterization of their dopants due to gradients in their dopant concentration and agglomeration of the material itself. Graphene’s two-dimensional nature allows for the detailed characterization of these dopants via spectroscopic and atomic resolution imaging techniques. Nitrogen doping of graphene has been well studied, providing insights into the dopant bonding structure, dopant-dopant interaction, and spatial segregation within a single crystal. Different configurations of nitrogen within the carbon lattice have different electronic and chemical properties, and by controlling these dopants it is possible to either n- or p-type dope graphene, grant half-metallicity, and alter nitrogen doped graphene’s (NG) catalytic and sensing properties. Thus, an understanding and the ability to control different types of nitrogen doping configurations allows for the fine tuning of NG’s properties. Here we review the synthesis, characterization, and properties of nitrogen dopants in NG beyond atomic dopant concentration.

Since the isolation of graphene in 2004, two-dimensional (2D) materials research has rapidly evolved into an entire subdiscipline in the physical sciences with a wide range of emergent applications. The unique 2D structure offers an open canvas to tailor and functionalize 2D materials through layer number, defects, morphology, moirépattern, strain, and other control knobs. Through this review, we aim to highlight the most recent discoveries in the following topics: theoryguided synthesis for enhanced control of 2D morphologies, quality, yield, as well as insights toward novel 2D materials; defect engineering to control and understand the role of various defects, including in situ and ex situ methods; and properties and applications that are related to moiréengineering, strain engineering, and artificial intelligence. Finally, we also provide our perspective on the challenges and opportunities in this fascinating field.

Graphene is a two-dimensional (2D) material consisting of a single sheet of sp2 hybridized carbon atoms laced in a hexagonal lattice, with potentially wide usage as a Raman enhancement substrate, also termed graphene-enhanced Raman scattering (GERS), making it ideal for sensing applications. GERS improves upon traditional surface-enhanced Raman scattering (SERS), combining its single-molecule sensitivity and spectral fingerprinting of molecules, and graphene’s simple processing and superior uniformity. This enables fast and highly sensitive detection of a wide variety of analytes. Accordingly, GERS has been investigated for a wide variety of sensing applications, including chemical- and bio-sensing. As a derivative of GERS, the use of two-dimensional materials other than graphene for Raman enhancement has emerged, which possess remarkably interesting properties and potential wider applications in combination with GERS. In this review, we first introduce various types of 2D materials, including graphene, MoS2, doped graphene, their properties, and synthesis. Then, we describe the principles of GERS and comprehensively explain how the GERS enhancement factors are influenced by molecular and 2D material properties. In the last section, we discuss the application of GERS in chemical- and bio-sensing, and the prospects of such a novel sensing method.

The study of Alzheimer’s disease (AD), the most common cause of dementia, faces challenges in terms of understanding the cause, monitoring the pathogenesis, and developing early diagnoses and effective treatments. Rapid and accurate identification of AD biomarkers in the brain is critical to providing key insights into AD and facilitating the development of early diagnosis methods. In this work, we developed a platform that enables a rapid screening of AD biomarkers by employing graphene-assisted Raman spectroscopy and machine learning interpretation in AD transgenic animal brains. Specifically, we collected Raman spectra on slices of mouse brains with and without AD and used machine learning to classify AD and non-AD spectra. By contacting monolayer graphene with the brain slices, the accuracy was increased from 77% to 98% in machine learning classification. Further, using a linear support vector machine (SVM), we identified a spectral feature importance map that reveals the importance of each Raman wavenumber in classifying AD and non-AD spectra. Based on this spectral feature importance map, we identified AD biomarkers including Aβ and tau proteins and other potential biomarkers, such as triolein, phosphatidylcholine, and actin, which have been confirmed by other biochemical studies. Our Raman–machine learning integrated method with interpretability will facilitate the study of AD and can be extended to other tissues and biofluids and for various other diseases.