Mass production of high-quality graphene with low cost is the footstone for its widespread practical applications. We present herein a self-limited growth approach for producing graphene powders by a small-methane-flow chemical vapour deposition process on naturally abundant and industrially widely used diatomite (biosilica) substrates. Distinct from the chemically exfoliated graphene, thus-produced biomorphic graphene is highly crystallized with atomic layer-thickness controllability, structural designability and less noncarbon impurities. In particular, the individual graphene microarchitectures preserve a three-dimensional naturally curved surface morphology of original diatom frustules, effectively overcoming the interlayer stacking and hence giving excellent dispersion performance in fabricating solution-processible electrodes. The graphene films derived from as-made graphene powders, compatible with either rod-coating, or inkjet and roll-to-roll printing techniques, exhibit much higher electrical conductivity (∼110,700 S m−1 at 80% transmittance) than previously reported solution-based counterparts. This work thus puts forward a practical route for low-cost mass production of various powdery two-dimensional materials.

Many studies of nanomaterials make non-systematic alterations of nanoparticle physicochemical properties. Given the immense size of the property space for nanomaterials, such approaches are not very useful in elucidating fundamental relationships between inherent physicochemical properties of these materials and their interactions with, and effects on, biological systems. Data driven artificial intelligence methods such as machine learning algorithms have proven highly effective in generating models with good predictivity and some degree of interpretability. They can provide a viable method of reducing or eliminating animal testing. However, careful experimental design with the modelling of the results in mind is a proven and efficient way of exploring large materials spaces. This approach, coupled with high speed automated experimental synthesis and characterization technologies now appearing, is the fastest route to developing models that regulatory bodies may find useful. We advocate greatly increased focus on systematic modification of physicochemical properties of nanoparticles combined with comprehensive biological evaluation and computational analysis. This is essential to obtain better mechanistic understanding of nano-bio interactions, and to derive quantitatively predictive and robust models for the properties of nanomaterials that have useful domains of applicability.

Surgeons face challenges in intraoperatively defining margin of brain tumors due to its infiltrative nature. Extracellular acidosis caused by metabolic reprogramming of cancer cells is a reliable marker for tumor infiltrative regions. Although the acidic margin‐guided surgery shows promise in improving surgical prognosis, its clinical transition is delayed by having the exogenous probes approved by the drug supervision authority. Here, an intelligent surface‐enhanced Raman scattering (SERS) navigation system delineating glioma acidic margins without administration of exogenous probes is reported. With assistance of this system, the metabolites at the tumor cutting edges can be nondestructively transferred within a water droplet to a SERS chip with pH sensitivity. Homemade deep learning model automatically processes the Raman spectra collected from the SERS chip and delineates the pH map of tumor resection bed with increased speed. Acidity correlated cancer cell density and proliferation level are demonstrated in tumor cutting edges of animal models and excised tissues from glioma patients. The overall survival of animal models post the SERS system guided surgery is significantly increased in comparison to the conventional strategy used in clinical practice. This SERS system holds the promise in accelerating clinical transition of acidic margin‐guided surgery for solid tumors with infiltrative nature.

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