2D titanium carbide (Ti 3 C 2 T x MXene) has potential application in flexible/ transparent conductors because of its metallic conductivity and solution processability. However, solution-processed Ti 3 C 2 T x films suffer from poor hydration stability and mechanical performance that stem from the presence of intercalants, which are unavoidably introduced during the preparation of Ti 3 C 2 T x suspension. A proton acid colloidal processing approach is developed to remove the extrinsic intercalants in Ti 3 C 2 T x film materials, producing pristine Ti 3 C 2 T x films with significantly enhanced conductivity, mechanical strength, and environmental stability. Typically, pristine Ti 3 C 2 T x films show more than twofold higher conductivity (10 400 S cm −1 vs 4620 S cm −1 ) and up to 11-and 32-times higher strength and strain energy at failure (112 MPa, 1,480 kJ m −3 , vs 10 MPa, 45 kJ m −3 ) than films prepared without proton acid processing. Simultaneously, the conductivity and mechanical integrity of pristine films are also largely retained during the long-term storage in H 2 O/O 2 environment. The improvement in mechanical performance and conductivity is originated from the intrinsic strong interaction between Ti 3 C 2 T x layers, and the absence of extrinsic intercalants makes pristine Ti 3 C 2 T x films stable in humidity by blocking the intercalation of H 2 O/O 2 . This method makes the material more competitive for real-world applications such as electromagnetic interference shielding.

Estimating ecosystem evapotranspiration (ET) is important to understanding the global water cycle and to study land-atmosphere interactions. We developed a physics constrained machine learning (ML) model (hybrid model) to estimate latent heat flux (LE), which conserves the surface energy budget. By comparing model predictions with observations at 82 eddy covariance tower sites, our hybrid model shows similar performance to the pure ML model in terms of mean metrics (e.g., mean absolute percent errors) but, importantly, the hybrid model conserves the surface energy balance, while the pure ML model does not. A second key result is that the hybrid model extrapolates much better than the pure ML model, emphasizing the benefits of combining physics with ML for increased generalizations. The hybrid model allows inferring the structural dependence of ET and surface resistance (r s ), and we find that vegetation height and soil moisture are the main regulators of ET and r s . Plain Language SummaryA physics constrained machine learning model is developed using the FLUXNET2015 Tier 1 data set. This new approach is able to effectively retrieve latent heat flux while constraining energy conservation in the surface energy budget. This hybrid model has better performance in extrapolation than a pure machine learning model. Key Points: • A physics-constrained machine learning model of evapotranspiration (hybrid model) is developed and trained using the FLUXNET 2015 data set • The evapotranspiration retrieved by the hybrid model is as accurate as pure machine learning model and also conserves surface energy balance • The hybrid model better reproduces extremes and thus better extrapolates compared to the pure machine learning approach Supporting Information:• Supporting Information S1• Figure S1 • Table S1

Nacre-like graphene films are prepared by evaporation-induced assembly of graphene oxide dispersions containing small amounts of cellulose nanocrystal (CNC), followed by chemical reduction with hydroiodic acid. CNC induces the formation of wrinkles on graphene sheets, greatly enhancing the mechanical properties of the resultant graphene films. The graphene films deliver an ultrahigh tensile strength of 765 ± 43 MPa (up to 800 MPa in some cases), a large failure strain of 6.22 ± 0.19%, and a superior toughness of 15.64 ± 2.20 MJ m , as well as a high electrical conductivity of 1105 ± 17 S cm . They have a great potential for applications in flexible electronics because of their combined excellent mechanical and electrical properties.

The hydrogel matrix normally forms via covalent or noncovalent interactions that make the matrix resistant to hydration and disassembly. Herein this type of chemical transition is demonstrated in titanium carbide MXene (Ti 3 C 2 T x ), in which the exchange of intercalated Li + with hydrated protons triggers significantly suppressed hydration in stacked Ti 3 C 2 T x . Based on this intercalation chemistry behavior, pristine Ti 3 C 2 T x hydrogel matrices with an arbitrary microstructures are fabricated by freezing-induced preassembly and a subsequent specially designed thawing process in protic acids. The absence of extrinsic components maximizes the materials performance of the resultant pristine Ti 3 C 2 T x hydrogel, which produces a compressive modulus of 2.4 MPa and conductivity of 220.3 ± 16.8 S/m at 5 wt % solid content. The anisotropic Ti 3 C 2 T x hydrogel also delivers a promising performance in solar steam generation by facilitating rapid water transport inside vertical microchannels.