Absorbance (410 nm) of supernatants of soil slurries after 300 s sonication at different extraction ratios as a function of power density.

<p>Absorbance (410 nm) of supernatants of soil slurries after 300 s sonication at different extraction ratios as a function of power density.</p

Activity of alkaline phosphatase of the autoclaved soil and the borax-borate buffer (control) with 50 mU external alkaline phosphatase as functions of sonication time.

<p>Activity of alkaline phosphatase of the autoclaved soil and the borax-borate buffer (control) with 50 mU external alkaline phosphatase as functions of sonication time.</p

Activity of alkaline phosphatase of the field-moist soil as a function of sonication time. Data are means ± SD (<i>n</i> = 3).

<p>Activity of alkaline phosphatase of the field-moist soil as a function of sonication time. Data are means ± SD (<i>n</i>  =  3).</p

Temperature of 100 ml deionized water as a function of sonication power.

<p>Temperature of 100 ml deionized water as a function of sonication power.</p

Total bacteria number of field-moist soil as a function of sonication time.

<p>Total bacteria number of field-moist soil as a function of sonication time.</p

Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life (RUL). However, this task is challenging due to the diverse ageing mechanisms, various operating conditions, and limited measured signals. Although data-driven methods are perceived as a promising solution, they ignore intrinsic battery physics, leading to compromised accuracy, low efficiency, and low interpretability. In response, this study integrates domain knowledge into deep learning to enhance the RUL prediction performance. We demonstrate accurate RUL prediction using only a single charging curve. First, a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging data. The parameters inform a deep neural network (DNN) to predict RUL with high accuracy and efficiency. The trained model is validated under 3 types of batteries working under 7 conditions, considering fully charged and partially charged cases. Using data from one cycle only, the proposed method achieves a root mean squared error (RMSE) of 11.42 cycles and a mean absolute relative error (MARE) of 3.19% on average, which are over 45% and 44% lower compared to the two state-of-the-art data-driven methods, respectively. Besides its accuracy, the proposed method also outperforms existing methods in terms of efficiency, input burden, and robustness. The inherent relationship between the model parameters and the battery degradation mechanism is further revealed, substantiating the intrinsic superiority of the proposed method.</p

Temporal in situ synmics of N20 reductase activity as affected by nitrogen fertilization and implications for the N20/(N20+N2) product ratio and N20 mitigation.

In vitro, high nitrate (NO3) concentrations significantly inhibit N2O reductase activity. However, little information is available on the in situ temporal effects of excessive N fertilization on soil N2O reductase activity and the regulation of the N2O/(N2 + N2O) product ratio in agricultural soil. This study examined the monthly in situ dynamics of NO3 − concentration, N2O reductase activity, and N2O/(N2 + N2O) product ratio for 2 years in loamy soil that had received either continuous N fertilizer at 400 kg N ha−1 year−1 for 15 years (N400) or no N fertilizers (CK). N2O reductase activity was significantly lower under the N400 treatment than under the CK and correlated negatively with soil NO3 − concentration. The decrease in N2O reductase activity resulted in the N2O/(N2 + N2O) product ratio increasing. These results demonstrate that excessive N fertilization has the potential to increase N2O emissions by reducing N2O reductase activity in soils. These results highlight the need for N2O mitigation options to embrace the reduction of soil NO3 − concentrations

MOESM4 of Root-associated microbiomes of wheat under the combined effect of plant development and nitrogen fertilization

Additional file 4. Dominant fungal orders (relative abundance > 1%) in the rhizosphere and root samples

Soil chemical properties in different soil depths across the 0‒5.2 m soil profiles in the N0 and N600 treatments.

Soil clay content (a), pH (b), soil organic carbon (c) and nitrate content (d) in different soil depths across the 0‒5.2 m soil profiles in the N0 and N600 treatments. N0 and N600 represent fertilizer N input rates of 0 and 600 kg N ha-1 year-1, respectively. Relative errors were less than 0.05 for all the measured parameters (n = 2).</p

Differential immediate and long-term effects of nitrogen input on denitrification N₂O/(N₂O +N₂) ratio along a 0–5.2 m soil profile

High nitrogen (N) input to soil can cause higher nitrous oxide (N2O) emissions, that is, a higher N2O/(N2O+N2) ratio, through an inhibition of N2O reductase activity and/or a decrease in soil pH. We assumed that there were two mechanisms for the effects of N input on N2O emissions, immediate and long-term effect. The immediate effect (field applied fertilizer N) can be eliminated by decreasing the N input, but not the long-term effect (soil accumulated N caused by long–term fertilization). Therefore, it is important to separate these effects to mitigate N2O emissions. To this end, soil samples along a 0–5.2 m profile were collected from a long-term N fertilization experiment field with two N application rates, that is, 600 kg N ha-1 year-1 (N600) and no fertilizer N input (N0). External N addition was conducted for each subsample in the laboratory incubation study to produce two additional treatments, which were denoted as N600+N and N0+N treatments. The results showed that the combined immediate and long-term effects led to an increase in the N2O/(N2O+N2) ratio by 6.8%. Approximately 32.6% and 67.4% of increase could be explained by the immediate and long-term effects of N input, respectively. Meanwhile, the long-term effects were significantly positively correlated to soil organic carbon (SOC). These results indicate that excessive N fertilizer input to the soil can lead to increased N2O emissions if the soil has a high SOC content. The long-term effect of N input on the N2O/(N2O+N2) ratio should be considered when predicting soil N2O emissions under global environmental change scenarios. </p