Nitrogen‐doped three‐dimensional graphene (N‐doped 3D‐graphene) is a graphene derivative with excellent adsorption capacity, large specific surface area, high porosity, and optoelectronic properties. Herein, N‐doped 3D‐graphene/Si heterojunctions were grown in situ directly on silicon (Si) substrates via plasma‐assisted chemical vapor deposition (PACVD), which is promising for surface‐enhanced Raman scattering (SERS) substrates candidates. Combined analyses of theoretical simulation, incorporating N atoms in 3D‐graphene are beneficial to increase the electronic state density of the system and enhance the charge transfer between the substrate and the target molecules. The enhancement of the optical and electric fields benefits from the stronger light‐matter interaction improved by the natural nano‐resonator structure of N‐doped 3D‐graphene. The as‐prepared SERS substrates based on N‐doped 3D‐graphene/Si heterojunctions achieve ultra‐low detection for various molecules: 10−8 M for methylene blue (MB) and 10−9 M for crystal violet (CRV) with rhodamine (R6G) of 10−10 M. In practical detected, 10−8 M thiram was precisely detected in apple peel extract. The results indicate that N‐doped 3D‐graphene/Si heterojunctions based‐SERS substrates have promising applications in low‐concentration molecular detection and food safety.

High-performance organic semiconductors (OSCs) can be designed based on the identification of functional units and their role in the material properties. Herein, we present a polymer-unit fingerprint (PUFp) generation framework, “Python-based polymer-unit-recognition script” (PURS), to identify the subunits “polymer unit” in the polymer and generate polymer-unit fingerprint (PUFp). Using 678 collected OSC data, machine learning (ML) models can be used to determine structure–mobility relationships by using PUFp as a structural input, and the classification accuracy reaches 85.2%. A polymer-unit library consisting of 445 units is constructed, and the key polymer units affecting the mobility of OSCs are identified. By investigating the combinations of polymer units with mobility performance, a scheme for designing OSCs by combining ML approaches and PUFp information is proposed. This scheme not only passively predicts OSC mobility but also actively provides structural guidance for high-mobility OSC material design. The proposed scheme demonstrates the ability to screen materials through pre-evaluation and classification ML steps and is an alternative methodology for applying ML in high-mobility OSC discovery.

2,2′-Bithiazolothienyl-4,4′,10,10′-tetracarboxydiimide (DTzTI), a novel imide-functionalized thiazole, is envisioned as a candidate for an excellent building block for constructing all-acceptor homopolymers, and the resulting PDTzTI, which is the polymer of DTzTI, demonstrated unipolar n-type transport with an exceptional electron mobility (μe) of 1.61 cm2 V–1 s–1. Density functional theory (DFT) and the incoherent charge-hopping model at the molecular level are used to design and investigate the model compounds DTzTI and two novel fluorine- or selenium-substituted analogues, DTzTI-2F and DTzTI-4Se, in order to better understand the roles of conjugation length and orbital delocalization for intrinsic charge transport as well as to increase the electron mobility and ambient stability of DTzTI-based polymers. According to the DFT results, increasing the conjugation length (n, number of haploids) of homopolymer molecules could significantly lower the recombination energy, decrease the E LUMO–HOMO, improve the delocalization of the frontier molecular orbitals, and raise the electron’s transfer integral (V e) between adjacent neighboring homopolymer molecules. This would make it easier to delocalize and transport charge carriers between chains, increasing the electron-transfer efficiency. Additionally, lowering the lowest unoccupied molecular orbital (LUMO) level below −4 eV with the substitution of fluorine or selenium would be very advantageous to ambient stability. 8DTzTI, 8DTzTI-2F, and 8DTzTI-4Se are anticipated to have μe values of 23.87, 19.44, and 29.07 cm2 V–1 s–1, respectively. The performance of all the three analogues is unipolar n-type. The bigger orbital delocalization and larger transfer integral resulting from the face-to-face π–π stacking produce significant electron mobility for DTzTI-4Se, demonstrating that larger delocalization of molecular orbitals will improve intermolecular conjugation and boost charge transport characteristics. A straightforward mathematical model of mobility and conjugation length is discussed, enabling a rapid computation of the theoretical mobilities for specific homopolymers of all-acceptor n-type semiconductor materials. Another method for enhancing the electron mobility and environmental stability of DTzTI-based unipolar n-type polymer semiconductors is selenium substitution.

The entire decomposition reaction process of a 30 Å HMX nanoparticle at 2000 K by ReaxFF molecular dynamics.

In the process of finding high-performance organic semiconductors (OSCs), it is of paramount importance in material development to identify important functional units that play key roles in material performance and subsequently establish substructure-property relationships. Herein, we describe a polymer-unit fingerprint (PUFp) generation framework. Machine learning (ML) models can be used to determine structure-mobility relationships by using PUFp information as structural input with 678 pieces of collected OSC data, and the prediction accuracy reaches 83.9%. A polymer-unit library consisting of 445 units is constructed, and the key polymer units for the mobility of OSCs are identified. By investigating the combinations of polymer units with mobility performance, a scheme for designing polymer OSC materials by combining ML approaches and PUFp information is proposed to not only passively predict OSC mobility but also actively provide structural guidance for new high-mobility OSC material design. The proposed scheme demonstrates the ability to screen new materials through pre-evaluation and classification ML steps and is an alternative methodology for applying ML in new high-mobility OSC discovery.