Designing Temperature-Insensitive Solvated Electrolytes for Low-Temperature Lithium Metal Batteries.

Lithium metal batteries face problems from sluggish charge transfer at interfaces, as well as parasitic reactions between lithium metal anodes and electrolytes, due to the strong electronegativity of oxygen donor solvents. These factors constrain the reversibility and kinetics of lithium metal batteries at low temperatures. Here, a nonsolvating cosolvent is applied to weaken the electronegativity of donor oxygen in ether solvents, enabling the participation of anionic donors in the solvation structure of Li+. This strategy significantly accelerates the desolvation process of Li+ and reduces the side effects of solvents on interfacial transport and stability. The designed anion-aggregated electrolyte has a unique temperature-insensitive solvation structure and enables lithium metal anodes to achieve a high average Coulombic efficiency at room temperature and -20 °C. A high-loading LiFePO4||Li cell exhibited high reversibility with a 100% capacity retention after 150 cycles at room temperature, -20, and -40 °C. The practical 1 Ah-level LiFePO4||Li pouch-cell delivered 81% and 61% of the capacity at room temperature when charged and discharged at -20 and -40 °C, respectively. This strategy of constructing temperature-insensitive solvation by electronegativity regulation offers a novel approach for developing electrolytes of low-temperature batteries.

Stable Oxyhalide-Nitride Fast Ionic Conductors for All-Solid-State Li Metal Batteries.

Rechargeable all-solid-state lithium metal batteries (ASSLMBs) utilizing inorganic solid-state electrolytes (SSEs) are promising for electric vehicles and large-scale grid energy storage. However, the Li dendrite growth in SSEs still constrains the practical utility of ASSLMBs. To achieve a high dendrite-suppression capability, SSEs must be chemically stable with Li, possess fast Li transfer kinetics, and exhibit high interface energy. Herein, a class of low-cost, eco-friendly, and sustainable oxyhalide-nitride solid electrolytes (ONSEs), denoted as LixNyIz-qLiOH (where x = 3y + z, 0 ≤ q ≤ 0.75), is designed to fulfill all the requirements. As-prepared ONSEs demonstrate chemically stable against Li and high interface energy (>43.08 meV Å-2), effectively restraining Li dendrite growth and the self-degradation at electrode interfaces. Furthermore, improved thermodynamic oxidation stability of ONSEs (>3 V vs Li+/Li, 0.45 V for pure Li3N), arising from the increased ionicity of Li─N bonds, contributes to the stability in ASSLMBs. As a proof-of-concept, the optimized ONSEs possess high ionic conductivity of 0.52 mS cm-1 and achieve long-term cycling of Li||Li symmetric cell for over 500 h. When coupled with the Li3InCl6 SSE for high-voltage cathodes, the bilayer oxyhalide-nitride/Li3InCl6 electrolyte imparts 90% capacity retention over 500 cycles for Li||1 mAh cm-2 LiCoO2 cells.

Ligand-channel-enabled ultrafast Li-ion conduction.

Li-ion batteries (LIBs) for electric vehicles and aviation demand high energy density, fast charging and a wide operating temperature range, which are virtually impossible because they require electrolytes to simultaneously have high ionic conductivity, low solvation energy and low melting point and form an anion-derived inorganic interphase1-5. Here we report guidelines for designing such electrolytes by using small-sized solvents with low solvation energy. The tiny solvent in the secondary solvation sheath pulls out the Li+ in the primary solvation sheath to form a fast ion-conduction ligand channel to enhance Li+ transport, while the small-sized solvent with low solvation energy also allows the anion to enter the first Li+ solvation shell to form an inorganic-rich interphase. The electrolyte-design concept is demonstrated by using fluoroacetonitrile (FAN) solvent. The electrolyte of 1.3 M lithium bis(fluorosulfonyl)imide (LiFSI) in FAN exhibits ultrahigh ionic conductivity of 40.3 mS cm-1 at 25 °C and 11.9 mS cm-1 even at -70 °C, thus enabling 4.5-V graphite||LiNi0.8Mn0.1Co0.1O2 pouch cells (1.2 Ah, 2.85 mAh cm-2) to achieve high reversibility (0.62 Ah) when the cells are charged and discharged even at -65 °C. The electrolyte with small-sized solvents enables LIBs to simultaneously achieve high energy density, fast charging and a wide operating temperature range, which is unattainable for the current electrolyte design but is highly desired for extreme LIBs. This mechanism is generalizable and can be expanded to other metal-ion battery electrolytes.

Tuning the Cathode-Electrolyte Interphase Chemistry with Multifunctional Additive for High-Voltage Li-Ion Batteries.

The utilization of layered oxides as cathode materials has significantly contributed to the advancement of the lithium-ion batteries (LIBs) with high energy density and reliability. However, the structural and interfacial instability triggered by side reactions when charged to high voltage has plagued their practical applications. Here, this work reports a novel multifunctional additive, id est, 7-Anilino-3-diethylamino-6-methyl fluoran (ADMF), which exhibits unique characteristics such as preferential adsorption, oxygen scavenging, and electropolymerization protection for high-voltage cathodes. The ADMF demonstrates the capability to ameliorate the growth of cathode-electrolyte interphase (CEI), effectively diminishing the dissolution of transition metal (TM) ions, reducing the interface impedance, and facilitating the Li+ transport. As a result, ADMF additive with side reaction-blocking ability significantly enhances the cycling stability of MCMB||NCM811 full-cells at 4.4 V and MCMB||LCO full-cells at 4.55 V, as evidenced by the 80% retention over 600 cycles and 87% retention after 750 cycles, respectively. These findings highlight the potential of the additive design strategy to modulate the CEI chemistry, representing a new paradigm with profound implications for the development of next-generation high-voltage LIBs.

Can network infrastructure construction improve the low-carbon innovation of Chinese enterprises?

This study considers the implementation of the "Broadband China" strategy as an exogenous policy shock and examines the impact of network infrastructure construction (NIC) on the low-carbon innovation (LCI) of enterprises and its underlying mechanisms by using a progressive difference-in-difference model based on the data of Chinese listed enterprises from 2009 to 2020. This study finds that NIC can improve the LCI of enterprises. After the elimination of the sample selection bias and selection of the urban slope as the exogenous instrumental variable, the conclusions remained robust. The results of the mechanism test show that upgrading the human capital level, reducing transaction costs, and alleviating financing constraints are the three important paths through which NIC can help enterprises improve their LCI level. The heterogeneity analysis determines that NIC has considerable comparative advantages for enterprises with executives who have a financial background and enterprises with high knowledge stock. In addition, LCI improvement can further enhance enterprise value. The research conclusions can broaden the microscopic research perspective of enterprise transformation and upgrading theory and provide reliable empirical evidence for China's low-carbon economic transformation.

Multifunctional solvent molecule design enables high-voltage Li-ion batteries.

Elevating the charging cut-off voltage is one of the efficient approaches to boost the energy density of Li-ion batteries (LIBs). However, this method is limited by the occurrence of severe parasitic reactions at the electrolyte/electrode interfaces. Herein, to address this issue, we design a non-flammable fluorinated sulfonate electrolyte by multifunctional solvent molecule design, which enables the formation of an inorganic-rich cathode electrolyte interphase (CEI) on high-voltage cathodes and a hybrid organic/inorganic solid electrolyte interphase (SEI) on the graphite anode. The electrolyte, consisting of 1.9 M LiFSI in a 1:2 v/v mixture of 2,2,2-trifluoroethyl trifluoromethanesulfonate and 2,2,2-trifluoroethyl methanesulfonate, endows 4.55 V-charged graphite||LiCoO2 and 4.6 V-charged graphite||NCM811 batteries with capacity retentions of 89% over 5329 cycles and 85% over 2002 cycles, respectively, thus resulting in energy density increases of 33% and 16% compared to those charged to 4.3 V. This work demonstrates a practical strategy for upgrading the commercial LIBs.

Effects of plant residues on C:N:P of soil, microbial biomass, and extracellular enzyme in an alpine mea-dow on the Qinghai-Tibetan Plateau, China.

Plant residues can affect C:N:P of soil, microbial biomass, and extracellular enzyme, but the effects are still unclear. We conducted a field experiment in an alpine meadow on the eastern part of the Qinghai-Tibetan Plateau to explore the effects of removing aboveground plant or roots and adding plant residues on the C:N:P of soil, microbial biomass, and extracellular enzyme. The results showed that removing aboveground plant biomass significantly decreased soil C:N (the change was -23.7%, the same below) and C:P (-14.7%), microbial biomass C:P and N:P, while significantly increased microbial biomass C:N, and enzyme C:N:P compared with meadow without human disturbance. Removing all plant biomass (aboveground and roots) significantly reduced soil C:N (-11.6%), C:P (-24.0%), N:P (-23.3%) and microbial biomass C:N in comparison to removing aboveground plant, while significantly improved microbial biomass N:P and enzyme N:P. Adding plant residues after removing aboveground plant significantly increased microbial biomass C:N and C:P, enzyme C:N compared with removing aboveground plant, while significantly decreased enzyme N:P. Compared with removing all the plant, adding plant residues after removing whole plant significantly reduced soil C:N (-16.4%), microbial biomass C:P, N:P and enzyme N:P, while significantly increased enzyme C:N. Our results suggest that removal of plants could have a strong effect on C:N:P of soil, microbial biomass, and extracellular enzyme, and C:N:P of microbial biomass and that extracellular enzyme woule be more sensitive to plant residues. Roots could play a key role in stabilizing C:N:P of soil, microbial biomass, and extracellular enzyme under plant residues addition. Adding plant residues could be a suitable solution for restoring alpine meadows under the circumstance of intact roots, which was conducive to soil C storage, but might not be suitable for alpine meadows with serious root damage, which would increase soil CO2 emission.

Simultaneous Stabilization of Lithium Anode and Cathode using Hyperconjugative Electrolytes for High-voltage Lithium Metal Batteries.

Although great progress has been made in new electrolytes for lithium metal batteries (LMBs), the intrinsic relationship between electrolyte composition and cell performance remains unclear due to the lack of valid quantization method. Here, we proposed the concept of negative center of electrostatic potential (NCESP) and Mayer bond order (MBO) to describe solvent capability, which highly relate to solvation structure and oxidation potential, respectively. Based on established principles, the selected electrolyte with 1.7 M LiFSI in methoxytrimethylsilane (MOTMS)/ (trifluoromethyl)trimethylsilane (TFMTMS) shows unique hyperconjugation nature to stabilize both Li anode and high-voltage cathode. The 4.6 V 30 µm Li||4.5 mAh cm-2 lithium cobalt oxide (LCO) (low N/P ratio of 1.3) cell with our electrolyte shows stable cycling with 91 % capacity retention over 200 cycles. The bottom-up design concept of electrolyte opens up a general strategy for advancing high-voltage LMBs.

Relationships between denitrification rates and functional gene abundance in a wetland: The roles of single- and multiple-species plant communities.

Wetland soil denitrification removes excess inorganic nitrogen (N) and prevents eutrophication in aquatic ecosystems. Wetland plants have been considered the key factors determining the capacity of wetland soil denitrification to remove N pollutants in aquatic ecosystems. However, the influences of various plant communities on wetland soil denitrification remain unknown. In the present study, we measured variations in soil denitrification under different herbaceous plant communities including single Phragmites karka (PK), single Paspalum thunbergia (PT), single Zizania latifolia (ZL), a mixture of Paspalum thunbergia plus Phragmites karka (PTPK), a mixture of Paspalum thunbergia plus Zizania latifolia (PTZL), and bare soil (CK) in the Estuary of Nantiaoxi River, the largest tributary of Qingshan Lake in Hangzhou, China. The soil denitrification rate was significantly higher in the surface (0-10 cm) than the subsurface (10-20 cm) layer. Wetland plant growth increased the soil denitrification rate by significantly increasing the soil water content, nitrate concentration, and ln(nirS) + ln(nirK). A structural equation model (SEM) showed that wetland plants indirectly regulated soil denitrification by altering the aboveground and belowground plant biomass, nitrate concentration, abundances of denitrifying functional genes, and denitrification potential. There was no significant difference in soil denitrification rates among PT, PK and ZL. The soil denitrification rate was significantly lower in PTZL than PTPK. Two-plant communities did not necessarily enhance the denitrification rate compared to single planting, the former had a greater competitiveness on N uptake and consequently reduced the amount of nitrate available for denitrification. As PTPK had the highest denitrification rate, co-planting P. thunbergia and P. karka could effectively improve N removal efficiency and help mitigate eutrophication in adjacent aquatic ecosystems. The results of this investigation provide useful information guiding the selection of appropriate wetland herbaceous plant species for wetland construction and the removal of N pollutants in aquatic ecosystems.

Long-term assessment on performance and seasonal optimal operation of a full-scale integrated multiple constructed wetland-pond system.

Constructed wetlands as natural process-based water treatment technologies are popular globally. However, lack of detailed long-term assessment on the impact of seasonal variations on their performance with focus on optimal seasonal adjustments of controllable operating parameters significantly limits their efficient and sustainable long-term operation. To address this, a full-scale integrated multiple surface flow constructed wetlands-pond system situated between slightly polluted river water and outflow-receiving waterworks in a subtropical monsoon climate area of middle-eastern China was seasonally assessed over a period of six years. During this period, the removal rate (R) and mass removal rate (MRR) of total nitrogen (TN), total phosphorus (TP) and chemical oxygen demand (COD) possessed strong seasonality (p < 0.05). The highest R (%) and MRR (mg/m2/d) were in summer for TN (51.53 %, 114.35), COD (16.30 %, 143.85) and TP (62.39 %, 23.89) and least in spring for TN (23.88 %, 39.36) and COD. Whereas for TP, the least R was in autumn (37.82 %) and least MRR was in winter (9.35). Applying a first-order kinetics model coupled with Spearman's rank correlation analysis, purification efficiency exhibited significant dependence on temperature as nutrient reaction rates constant, k generally increased with temperature and was highest in summer. Meanwhile, the R of TN, TP and COD were positively correlated with influent concentration whiles MRR of TP was negatively correlated with hydraulic retention time but positively correlated with hydraulic loading rate (HLR) (p < 0.05). Also, MRR of COD and TN were positively correlated with mass loading rates (MLR) in summer and autumn. Through linear optimization, the best operating parameters according to the compliance rate were determined and a set of guidelines were proposed to determine the optimal operational change of hydrological index in each season (Spring, 0.1-0.12 m/d; Summer, 0.14-0.16 m/d; Autumn, 0.15-0.17 m/d; Winter, 0.1-0.11 m/d) for efficient and sustainable long-term operation.

Tackling realistic Li+ flux for high-energy lithium metal batteries.

Electrolyte engineering advances Li metal batteries (LMBs) with high Coulombic efficiency (CE) by constructing LiF-rich solid electrolyte interphase (SEI). However, the low conductivity of LiF disturbs Li+ diffusion across SEI, thus inducing Li+ transfer-driven dendritic deposition. In this work, we establish a mechanistic model to decipher how the SEI affects Li plating in high-fluorine electrolytes. The presented theory depicts a linear correlation between the capacity loss and current density to identify the slope k (determined by Li+ mobility of SEI components) as an indicator for describing the homogeneity of Li+ flux across SEI, while the intercept dictates the maximum CE that electrolytes can achieve. This model inspires the design of an efficient electrolyte that generates dual-halide SEI to homogenize Li+ distribution and Li deposition. The model-driven protocol offers a promising energetic analysis to evaluate the compatibility of electrolytes to Li anode, thus guiding the design of promising electrolytes for LMBs.

Enhanced soil potential N2O emissions by land-use change are linked to AOB-amoA and nirK gene abundances and denitrifying enzyme activity in subtropics.

Conversion of forestland to intensively managed agricultural land occurs worldwide and can increase soil nitrous oxide (N2O) emissions by altering the transformation processes of nitrogen (N) cycling related microbes and environmental conditions. However, little research has been conducted to assess the relationships between nitrifying and denitrifying functional genes and enzyme activities, the altered soil environment and N2O emissions under forest conversion in subtropical China. Here, we investigated the long-term (two decades) effect of converting natural forests to intensively managed tea (Camellia sinensis L.) plantations on soil potential N2O emissions, inorganic N concentrations, functional gene abundances of nitrifying and denitrifying bacteria, as well as nitrifying and denitrifying enzyme activities in subtropical China. The conversion significantly increased soil potential N2O emissions, which were regulated directly by increased denitrifying enzyme activity (52 %) and nirS + nirK gene abundance (38 %) as shown by structural equation modeling, and indirectly by AOB-amoA gene abundance and inorganic N concentration. Our results indicate that converting natural forests to tea plantations directly increases soil inorganic N concentration, resulting in increases in the abundance of soil nitrifying and denitrifying microorganisms and the associated N2O emissions. These findings are crucial for disentangling the factors that directly and indirectly affect soil potential N2O emissions respond to the conversion of forest to tea plantation.

A graphene oxide scaffold-encapsulated microcapsule for polysulfide-immobilized long life lithium-sulfur batteries.

Engineering high-performance cathodes for high energy-density lithium-sulfur (Li-S) batteries is quite significant to achieve commercialization. Here, we develop a graphene oxide scaffold/sulfur composite-encapsulated microcapsule (GSM) for high-performance Li-S batteries, which is prepared through the co-flow focusing (CFF) approach. The GSM-based cathode displays a high capacity of 1004 mA h g-1 at 0.2C after cycling 200 times, a long-term cycling stability after 1000 cycles at 2C, and a good rate-performance. At temperatures of -5 °C and 45 °C, the electrochemical performance is also excellent. The computational calculations based on density functional theory (DFT) verify the high adsorption energies of the microcapsules towards polysulfides, suppressing the shuttle effect efficiently. It is expected that the GSM system developed based on the CFF method here and its high electrochemical performance will enable it to be applicable for preparing many other emerging energy-storage materials and secondary batteries.

Land-Use Change Enhanced SOC Mineralization but Did Not Significantly Affect Its Storage in the Surface Layer.

To achieve carbon (C) neutrality and mitigate climate change, it is crucial to understand how converting natural forests to agricultural plantations influences soil organic C (SOC) mineralization. In this study, we investigated the impact of converting evergreen broadleaf forests (EBF) to extensively managed Moso bamboo (Phyllostachys edulis (Carriere) J. Houzeau) plantations (MBP) in subtropical China on SOC mineralization rate; the concentrations of labile SOC fractions such as dissolved organic C (DOC), microbial biomass C (MBC), and readily oxidizable C (ROC); the activities of C-degrading enzymes (cellobiohydrolase and phenol oxidase); and the abundance of C-degrading enzyme-encoding genes (cbhI and lcc). Three paired soil samples were taken from the surface layer (0-20 cm) of adjacent EBF-MBP sites in Anji County, Zhejiang province. Results showed that converting EBF to MBP significantly increased the SOC mineralization rate as well as soil pH, MBC, cellobiohydrolase, and phenol oxidase activities, and cbhI gene abundance, but did not change other soil properties described above. In addition, structural equation modelling (SEM) showed that the conversion increased SOC mineralization rate through increasing soil pH, cbhI gene abundance, MBC, and cellobiohydrolase and phenol oxidase activities. Our novel finding that converting EBF to extensively managed MBP enhanced SOC mineralization via increasing the activities of C-degrading enzymes suggests that C-degrading enzymes were a key factor regulating SOC mineralization in the extensively managed subtropical bamboo plantations.

A radial basis function neural network based multi-objective optimization for simultaneously enhanced nitrogen and phosphorus removal in a full-scale integrated surface flow treatment wetland-pond system.

In this study, a radial basis function neural network (RBFNN) model was developed and implemented in a multi-objective optimization procedure to determine the optimal hydraulic loading rate (HLR), hydraulic retention time (HRT), and mass loading rates (MLR) for enhanced removal of nitrogen and phosphorus by an integrated surface flow treatment wetland-pond system treating drinking source water in Yancheng, China. Prior to modelling, the system's 6-year nitrogen and phosphorus removal efficiencies were found to trend downwards as effluent concentrations trended positively. Meanwhile, operating parameter interaction effects impacted final effluent quality. Thus, total nitrogen and total phosphorus removal were simulated by an RBFNN model with satisfactory R2 of 0.99 and 0.98 respectively. Optimal average HLR, HRT and MLR for 80% simultaneous removal efficiencies were subsequently determined to be 0.10860 ± 0.03 md-1, 30.43 ± 9.96 d and 306.416 ± 89.54 mgm-2d-1 respectively. The results highlight the feasibility of the RBFNN modelling based optimization procedure for treatment wetlands.

EZH2 Inhibition Compromises α4-1BB-Mediated Antitumor Efficacy by Reducing the Survival and Effector Programming of CD8+ T Cells.

Enhancer of Zeste Homolog 2 (EZH2) inhibitors (EZH2i) are approved to treat certain cancer types. Previous studies have suggested the potential to combine EZH2i with immune checkpoint blockade targeting coinhibitory receptors like PD-(L)1 and CTLA-4, but whether it can also enhance the activity of agents targeting costimulatory receptors is not known. Here, we explore the combination between EZH2i and an agonist antibody targeting the T cell costimulatory receptor 4-1BB (α4-1BB). Our data show that EZH2i compromise the efficacy of α4-1BB in both CT26 colon carcinoma and in an in vivo protein immunization model. We link this to reduced effector survival and increased BIM expression in CD8+ T cells upon EZH2i treatment. These data support the requirement of EZH2 function in 4-1BB-mediated CD8+ T cell expansion and effector programming and emphasize the consideration that must be given when combining such antitumoral therapies.

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Soil denitrifying enzyme activity (DEA) was measured by acetylene inhibition technique, along with exploration of factors influencing DEA in a bamboo forest riparian zone in the upper reaches of the Taihu Lake Basin during summer. Our aim was to provide important insights into the assessment of ecological functions of bamboo forest riparian zone on reducing nitrogen pollution in rivers. The results showed that the riparian soil DEA ranged from 6.32 to 23.22 µg N·kg-1·h-1, with a mean value of 14.65 µg N·kg-1·h-1. The vertical distribution (0-40 cm soil profile) of DEA was affected by several factors, such as soil organic carbon (SOC), total nitrogen (TN), nitrate nitrogen (NO3--N), soil water content, and activity of carbon and nitrogen hydrolase, which resulted in decreased DEA with increasing soil depth. The horizontal changes in DEA (at the same soil depth but at different distances from river) was mainly governed by the variation in SOC concentration. In this area, the concentration of soil dissolved organic carbon was relatively low, which might inhibit the soil DEA during summer.

Cellular Origins of EGFR-Driven Lung Cancer Cells Determine Sensitivity to Therapy.

Targeting the epidermal growth factor receptor (EGFR) with tyrosine kinase inhibitors (TKIs) is one of the major precision medicine treatment options for lung adenocarcinoma. Due to common development of drug resistance to first- and second-generation TKIs, third-generation inhibitors, including osimertinib and rociletinib, have been developed. A model of EGFR-driven lung cancer and a method to develop tumors of distinct epigenetic states through 3D organotypic cultures are described here. It is discovered that activation of the EGFR T790M/L858R mutation in lung epithelial cells can drive lung cancers with alveolar or bronchiolar features, which can originate from alveolar type 2 (AT2) cells or bronchioalveolar stem cells, but not basal cells or club cells of the trachea. It is also demonstrated that these clones are able to retain their epigenetic differences through passaging orthotopically in mice and crucially that they have distinct drug vulnerabilities. This work serves as a blueprint for exploring how epigenetics can be used to stratify patients for precision medicine decisions.

Machine-Learning-Enabled Tricks of the Trade for Rapid Host Material Discovery in Li-S Battery.

The shuttle effect has been a major obstacle to the development of lithium-sulfur batteries. The discovery of new host materials is essential, but lengthy and complex experimental studies are inefficient for the identification of potential host materials. We proposed a machine learning method for the rapid discovery of an AB2-type sulfur host material to suppress the shuttle effect using the 2DMatPedia database, discovering 14 new structures (PdN2, TaS2, PtN2, TaSe2, AgCl2, NbSe2, TaTe2, AgF2, NiN2, AuS2, TmI2, NbTe2, NiBi2, and AuBr2) from 1320 AB2-type compounds. These structures have strong adsorptions of greater than 1.0 eV for lithium polysulfides and appreciable electron-transportation capability, which can serve as the most promising AB2-type host materials in lithium-sulfur batteries. On the basis of a small data set, we successfully predicted Li2S6 adsorption at arbitrary sites on substrate materials using transfer learning, with a considerably low mean absolute error (below 0.05 eV). The proposed data-driven method, as accurate as density functional theory calculations, significantly shortens the research cycle of screening AB2-type sulfur host materials by approximately 8 years. This method provides high-precision and expeditious solutions for other high-throughput calculations and material screenings based on adsorption energy predictions.

Mouse lung automated segmentation tool for quantifying lung tumors after micro-computed tomography.

Unlike the majority of cancers, survival for lung cancer has not shown much improvement since the early 1970s and survival rates remain low. Genetically engineered mice tumor models are of high translational relevance as we can generate tissue specific mutations which are observed in lung cancer patients. Since these tumors cannot be detected and quantified by traditional methods, we use micro-computed tomography imaging for longitudinal evaluation and to measure response to therapy. Conventionally, we analyze microCT images of lung cancer via a manual segmentation. Manual segmentation is time-consuming and sensitive to intra- and inter-analyst variation. To overcome the limitations of manual segmentation, we set out to develop a fully-automated alternative, the Mouse Lung Automated Segmentation Tool (MLAST). MLAST locates the thoracic region of interest, thresholds and categorizes the lung field into three tissue categories: soft tissue, intermediate, and lung. An increase in the tumor burden was measured by a decrease in lung volume with a simultaneous increase in soft and intermediate tissue quantities. MLAST segmentation was validated against three methods: manual scoring, manual segmentation, and histology. MLAST was applied in an efficacy trial using a Kras/Lkb1 non-small cell lung cancer model and demonstrated adequate precision and sensitivity in quantifying tumor growth inhibition after drug treatment. Implementation of MLAST has considerably accelerated the microCT data analysis, allowing for larger study sizes and mid-study readouts. This study illustrates how automated image analysis tools for large datasets can be used in preclinical imaging to deliver high throughput and quantitative results.