Hyponatremia, serum sodium 〈 135 mEq/L, is themost common electrolyte abnormality and is in a state of flux. Hyponatremic patients are symptomatic and should be treated but our inability to consistently determine the...Hyponatremia, serum sodium 〈 135 mEq/L, is themost common electrolyte abnormality and is in a state of flux. Hyponatremic patients are symptomatic and should be treated but our inability to consistently determine the causes of hyponatremia has hampered the delivery of appropriate therapy. This is especially applicable to differentiating syndrome of inappropriate antidiuresis （SIAD） from cerebral salt wasting （CSW） or more appropriately, renal salt wasting （RSW）, because of divergent therapeutic goals, to water-restrict in SIAD and administer salt and water in RSW. Differentiating SIAD from RSW is extremely diffcult because of identical clinical parameters that defne both syndromes and the mindset that CSW occurs rarely. It is thus insuffcient to make the diagnosis of SIAD simply because it meets the defned characteristics. We review the pathophysiology of SIAD and RSW, the evolution of an algorithm that is based on determinations of fractional excretion of urate and distinctive responses to saline infusions to differentiate SIAD from RSW. This algorithm also simplifes the diagnosis of hyponatremic patients due to Addison’s disease, reset osmostat and prerenal states. It is a common perception that we cannot accurately assess the volume status of a patient by clinical criteria. Our algorithm eliminates the need to determine the volume status with the realization that too many factors affect plasma renin, aldosterone, atrial/brain natriuretic peptide or urine sodium concentration to be useful. Reports and increasing recognition of RSW occurring in patients without evidence of cerebral disease should thus elicit the need to consider RSW in a broader group of patients and to question any diagnosis of SIAD. Based on the accumulation of supporting data, we make the clinically important proposal to change CSW to RSW, to eliminate reset osmostat as type C SIAD and stress the need for a new defnition of SIAD.展开更多
The current study focuses on the electrolyte penetration of the graphite cathode in a NaF−KF−LiF−AlF_(3) aluminum-electrolysis system with a cryolite ratio of 1.3.It involves a comprehensive investigation of the elect...The current study focuses on the electrolyte penetration of the graphite cathode in a NaF−KF−LiF−AlF_(3) aluminum-electrolysis system with a cryolite ratio of 1.3.It involves a comprehensive investigation of the electrolyte in the cathode before and after electrolysis by X-ray diffraction and analysis of the results by semi-quantitative calculation in MAUD.The results show that KF can promote electrolyte penetration,with higher KF contents resulting in greater penetration.During electrolyte penetration,K_(2)NaAlF_(6) and solid solutions containing KF play important roles in KF-containing systems.LiF effectively prevents the electrolyte penetration,while the Na_(3)Li_(3)Al_(2)F_(12) phase plays an essential role in systems with high LiF contents.展开更多
In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage,solid-state battery technology is attracting much research and attention.Solid-state electrolytes,as the ke...In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage,solid-state battery technology is attracting much research and attention.Solid-state electrolytes,as the key component of next-generation battery technology,are favored for their high safety,high energy density,and long life.However,finding high-performance solid-state electrolytes is the primary challenge for solid-state battery applications.Focusing on inorganic solid-state electrolytes,this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity,good thermal stability,and structural and phase stability.Traditional experimental and theoretical computational methods suffer from inefficiency,thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and characteristics.Through the gradient descent-based XGBoost algorithm,we successfully predicted the energy band structure and stability of the materials,and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously,which greatly accelerated the development of solid-state batteries.展开更多
文摘Hyponatremia, serum sodium 〈 135 mEq/L, is themost common electrolyte abnormality and is in a state of flux. Hyponatremic patients are symptomatic and should be treated but our inability to consistently determine the causes of hyponatremia has hampered the delivery of appropriate therapy. This is especially applicable to differentiating syndrome of inappropriate antidiuresis （SIAD） from cerebral salt wasting （CSW） or more appropriately, renal salt wasting （RSW）, because of divergent therapeutic goals, to water-restrict in SIAD and administer salt and water in RSW. Differentiating SIAD from RSW is extremely diffcult because of identical clinical parameters that defne both syndromes and the mindset that CSW occurs rarely. It is thus insuffcient to make the diagnosis of SIAD simply because it meets the defned characteristics. We review the pathophysiology of SIAD and RSW, the evolution of an algorithm that is based on determinations of fractional excretion of urate and distinctive responses to saline infusions to differentiate SIAD from RSW. This algorithm also simplifes the diagnosis of hyponatremic patients due to Addison’s disease, reset osmostat and prerenal states. It is a common perception that we cannot accurately assess the volume status of a patient by clinical criteria. Our algorithm eliminates the need to determine the volume status with the realization that too many factors affect plasma renin, aldosterone, atrial/brain natriuretic peptide or urine sodium concentration to be useful. Reports and increasing recognition of RSW occurring in patients without evidence of cerebral disease should thus elicit the need to consider RSW in a broader group of patients and to question any diagnosis of SIAD. Based on the accumulation of supporting data, we make the clinically important proposal to change CSW to RSW, to eliminate reset osmostat as type C SIAD and stress the need for a new defnition of SIAD.
基金financial supports from the National Natural Science Foundation of China (Nos.51774080,22078056)the National Key R&D Program of China (No.2018YFC1901905)。
文摘The current study focuses on the electrolyte penetration of the graphite cathode in a NaF−KF−LiF−AlF_(3) aluminum-electrolysis system with a cryolite ratio of 1.3.It involves a comprehensive investigation of the electrolyte in the cathode before and after electrolysis by X-ray diffraction and analysis of the results by semi-quantitative calculation in MAUD.The results show that KF can promote electrolyte penetration,with higher KF contents resulting in greater penetration.During electrolyte penetration,K_(2)NaAlF_(6) and solid solutions containing KF play important roles in KF-containing systems.LiF effectively prevents the electrolyte penetration,while the Na_(3)Li_(3)Al_(2)F_(12) phase plays an essential role in systems with high LiF contents.
基金supported by the National Natural Science Foundation of China(No.21421063,No.21473166,No.21573211,No.21633007,No.21790350,No.21803067,No.91950207)the Chinese Academy of Sciences(QYZDB-SSW-SLH018)+3 种基金the Anhui Initiative in Quantum Information Technologies(AHY090200)the USTC-NSRL Joint Funds(UN2018LHJJ)the Anhui Provincial Natural Science Foundation(2108085QB63)Numerical Theoretical simulations were done in the Supercomputing Center of USTC.
文摘In the current aera of rapid development in the field of electric vehicles and electrochemical energy storage,solid-state battery technology is attracting much research and attention.Solid-state electrolytes,as the key component of next-generation battery technology,are favored for their high safety,high energy density,and long life.However,finding high-performance solid-state electrolytes is the primary challenge for solid-state battery applications.Focusing on inorganic solid-state electrolytes,this work highlights the need for ideal solid-state electrolytes to have low electronic conductivity,good thermal stability,and structural and phase stability.Traditional experimental and theoretical computational methods suffer from inefficiency,thus machine learning methods become a novel path to intelligently predict material properties by analyzing a large number of inorganic structural properties and characteristics.Through the gradient descent-based XGBoost algorithm,we successfully predicted the energy band structure and stability of the materials,and screened out only 194 ideal solid-state electrolyte structures from more than 6000 structures that satisfy the requirements of low electronic conductivity and stability simultaneously,which greatly accelerated the development of solid-state batteries.
