Arsenophonus (A) and Wolbachia (W), affect virulence and fecundity in soybean aphid biotypes Bt1 and Bt3 cultured on whole plants and detached leaves of three resistant, Rag1, Rag2 and Rag1 + 2, and one susceptible, W82, soybean genotypes. Whole plants and individual aphid experiments of A. glycines with and without Arsenophonus and Wolbachia did not show differences in overall fecundity. Differences were observed in peak fecundity, first day of deposition, and day of maximum nymph deposition of individual aphids on detached leaves. Bt3 had higher fecundity than Bt1 on detached leaves of all plant genotypes regardless of bacterial profile. Symbionts did not affect peak fecundity of Bt1 but increased it in Bt3 (A+W+) and all Bt3 strains began to deposit nymphs earlier than the Bt1 (A+W−). Arsenophonus in Bt1 delayed the first day of nymph deposition in comparison to aposymbiotic Bt1 except when reared on Rag1 + 2. For the Bt1 and Bt3 strains, symbionts did not result in a significant difference in the day they deposited the maximum number of nymphs nor was there a difference in survival or variability in number of nymphs deposited. Variability of number of aphids deposited was higher in aphids feeding on resistant plant genotypes. The impact of Arsenophonus on soybean aphid patterns of fecundity was dependent on the aphid biotype and plant genotype. Wolbachia alone had no detectable impact but may have contributed to the increased fecundity of Bt3 (A+W+). An individual based model, using data from the detached leaves experiment and with intraspecific competition removed, found patterns similar to those observed in the greenhouse and growth chamber experiments including a significant interaction between soybean genotype and aphid strain. Combining individual data with the individual based model of population growth isolated the impact of fecundity and host resistance from intraspecific competition and host health. Changes to patterns of fecundity, influenced by the composition and concentration of symbionts, may contribute to competitive interactions among aphid genotypes and influence selection on virulent aphid populations.Plant breeding is used to develop crops with host resistance to aphids, however, virulent biotypes often develop that overcome host resistance genes. We tested whether the symbionts,  Buchnera aphidicola  is native to Eastern Asia (Rhamnus cathartica L. (Buckthorn)  , and by ispersal . As obseispersal , the soyispersal . If leftispersal . Soybeanispersal . Howeverispersal . Sustainispersal  and hostispersal . Howeverispersal .Diuraphis noxia, the Russian wheat aphid, on wheat   . Arsenopisolines .Wolbachia, as well as Hamiltonella, and several extracellular bacteria  (Hemiptera: Cixiidae) and Pentastiridius leporinus (L.) (Hemiptera: Cixiidae) respectively . A third experiment consisted of individual aphids reared on single detached plant leaves in petri dishes to determine the fecundity of individual aphids (detached leaves experiment). Analysis of the detached leaves experiment included an individual based model simulating the whole plant experiments using the detached leaves data.Rag1), (2) LD11-5431a (Rag2), (3) LD10-30014 , and (4) Williams 82 (W82) a line that has no known resistance to soybean aphid feeding.All experiments used four soybean plant genotypes obtained from Brian Diers at the University of Illinois. Three were soybean aphid-resistant lines: (1) LD11-4576a (A+W−), avirulent on Rag1, Rag2 and Rag1 + 2 soybean plants . (2) Bt1 (A−W−), is an isofemale line derived from Bt1 via the microinjection of ampicillin . (3) Bt3 (A+W+), is avirulent on Rag1 and virulent on Rag2 soybean plants  Bt3 (A−W+) strain was derived from Bt3 via microinjection with ampicillin  while (5) Bt3 (A−W−) was derived by the microinjection of Bt3 with doxycycline hyclate . It was not possible to clear the strain of Wolbachia without also clearing Arsenophonus. We therefore could not generate a Bt3 (A+W−) strain. Results from the screening and curing of aphid strains can be found in Five soybean aphid strains were used with varying symbiont profiles : (1) Bt1n plants ,b, was cn plants . All strn plants  and has Soybean aphid strains used in all experiments were cultured on detached leaves of soybean variety W82 placed in petri dishes (100 × 20 mm) at 25°C under a 16 h photoperiod. Fifteen to 20 apterous adult females were placed on each leaf and allowed to lay nymphs. Twenty-two to three-day-old nymphs were transferred to fresh soybean leaves and reared to adulthood. Offspring that were 2–3 day old produced by this second generation of nymphs were utilized for the experiments.2 following planting. Three seeds of each soybean genotype were planted per pot and thinned to one plant per pot after emergence, then grown to the VC stage for use in growth chamber or greenhouse experiments or V1–V3 stage for use in detached leaf experiments.All plants were grown in 13 cm diameter plastic pots using soil-less medium  and 15 mL of slow-release fertilizer pellets (Osmocote 19-6-12) spread evenly over the growth medium to an approximate density of 2–3 pellets per cm2s−1 PAR fluorescent and incandescent lamps programmed for a 16-h photoperiod at a constant 22°C. Plants remained in the same growth chamber under the same conditions after the application of aphids and containment cages.Plants for the growth chamber experiment were reared in a Conviron PGR15  illuminated with 500 μmol mGrowth chamber experiment cages consisted of a clear flexible plastic tube 10.5 × 45 cm with an opaque plastic top and two opposing, rectangular side silk  panels of 6 × 25.5 cm placed 3 cm from the top for ventilation. Cages for the greenhouse experiment were 17.78 × 17.78 × 40.64 cm and consisted of a wood frame and bottom with a plexiglass top and paneled on all four sides with silk. For both experiments, single VC-age plants were placed inside each cage and inoculated with 20 soybean aphid nymphs. Aphid populations on whole plants were enumerated 14 days after inoculation. We used a nonparametric scale to rate the health of the plants based on that of 2/s, from sixteen 17 W cool white, fluorescent lamps and two 25 W incandescent lamps with a cycle of 16 h light and 8 h dark.In the detached leaves experiment, fecundity of individual aphids was followed through their entire life cycle. For this experiment single unifoliate leaves with petioles were obtained from plants in the V1 or V2 stage of each variety and placed in individual petri dishes (100 × 20 mm) with a small cotton pad imbued with water wrapped around the petiole. A single aphid was placed on the top  surface of each leaf with a fine sable paintbrush. Petri dishes were wrapped around the edge and sealed with parafilm . Dishes were arranged on stainless steel trays so that leaves were fully illuminated and their order on the trays was rotated daily. To ensure that no contamination occurred between aphid strains in separate dishes, each strain was placed in a separate incubator. Trays were rotated daily in each chamber. Petri dishes were monitored daily, leaves were changed every 4 days, and deposited nymphs were counted and removed daily for 18 days. Petri dishes in which aphids trapped themselves in the cotton and died were eliminated from the study. The experiment was conducted at 25°C in Percival reach-in plant growth chambers, Model E-22 L, with a light intensity of 500 micromoles/mA−W−) reared on the susceptible W82 soybean leaves that had 13 replicates. Two trials, run at different times, were conducted for the plant growth chamber experiment and a single trial was conducted in the greenhouse.The greenhouse and growth chamber experiments used three replicates of each of the 20 treatments (four soybean genotypes and five aphid strains) for a total of 60 plants per experiment. For the greenhouse experiment, caged pots were placed in pairs, in trays without holes and arranged using a randomized block design to account for the pairing. For the detached leaf experiment, all 20 treatments (four soybean genotypes and five aphid strains) were included. The experiment began with 12 replicates for each treatment, except for Bt3 , and Bt3 (A+W+), was determined in the following manner: DNA was extracted from freshly killed aphids using the DNA Micro Kit  following the manufacturer’s protocol but with the following two changes: a 10 min incubation at 70°C after addition of the lysis buffer (AL) and the use of Wizard SV Mini columns (Promega) as these give a higher DNA yield. Specimens were macerated with a polypropylene pestle (Bel-Art Products) while viewing the specimen under a dissecting scope. Individual aphid specimens were tested with Wolbachia and Arsenophonus specific primers both as a screening tool during the process of generating the cured Bt1 and Bt3 lines as well as to confirm the infection profile of a subset of the initial and final aphids in the whole plant experiments and all the aphids that survived to the end of the detached leaves experiment. The bacterial screening primers and their respective protocols used were as follows: (1) Wolbachia screen: dnaA 2F (5′-acaattggttatatcagctg-3′) and dnaA 2R (5′-tacatagctatttgygttgg-3′) (Arsenophonus screen: Gly1-2F (5′-cgcgtmaagccaatctaagattg-3′) designed for this work and 480R (“-cacggtactggttcactatcggtc-3′) (The infection profile of test ttgg-3′)  (95°C 3 ggtc-3′)  (95°C 3 ggtc-3′) . PCR’s wWe developed a simple population model in SAS IML  which moThis simple model incorporates the cumulative impact of all aspects of the fecundity curves to produce an idealized fecundity rate based upon optimal conditions and does not include intraspecific competition. The model therefore isolates the impact of the different soybean varieties on aphid population growth.Prior to analysis, aphid population counts were transformed by adding one and taking the log base 10 to correct for non-constant variance among the treatments. Variance homogeneity tests indicated that the variance between the two trials were not significant, therefore data from both trials were combined in the final analysis.In the greenhouse experiment, cages were set up in pairs as part of a randomized complete block design. Cage pair was therefore used as a random factor in the greenhouse model. Tukey–Kramer adjustments were used for post-hoc analyses.Several methods were tried to transform the count data from the fecundity experiment conducted in the growth chamber and greenhouse. The data were overdispersed when analyzed using a Poisson model, therefore, we used a generalized linear model  with a negative binomial distribution and a log link function as suggested by We characterized overall fecundity , as well as the pattern of fecundity , and survival rate. We also tested whether there was a pattern of variability in fecundity among the different aphid strains on the different soybean genotypes.For the four measures that were analyzed, (1) total number of nymphs, (2) maximum number of nymphs, (3) day of first nymph, and (4) the day of maximum nymphs deposited, transformations were unsuccessful at normalizing the data or reducing hetero-skedasticity. For the same measures listed above, if the interaction term of soybean variety by aphid strain was not found to be significant, the analysis was rerun without the interaction term.A generalized linear model  with a negative binomial distribution and a log link function as suggested by W82 soybean genotype to each of the three other genotypes. Contrasts also compared lab aphid strains Bt1 (A+W−) to Bt3 (A+W+), as well as each of these strains to their derived antibiotic treated strains: Bt1 (A+W−) to and Bt1 (A−W−) and Bt3 (A+W+) to Bt3 (A−W−) and Bt3 (A−W+), and the two latter antibiotic treated strains to each other. A Bonferroni adjustment was used to determine alpha for multiple comparisons.A weighted least squares approach, modeling the mean response, was used for the analysis . Contrasts compare the maximum number of aphids deposited on the susceptible A weighted least squares approach was not an acceptable analysis for comparing the first day that aphids were deposited due to a problem with linear dependence. Cochran–Mantel–Haenszel row mean score  was therefore used to assess the effect of soybean genotype and aphid strain on first day of nymph deposition. If an aphid did not deposit nymphs, it was removed from the analysis.We analyzed the day that the maximum number of nymphs were deposited using a generalized linear model  with a negative binomial distribution with an identify link function. The Poisson distribution resulted in an overdispersed model and a log link resulted in failure of the relative Hession convergence criteria. Tukey–Kramer post-hoc analyses were used to compare least square means.Two models were used to assess the effect of soybean genotype on the survival of different strains of aphids. A logistic regression event/trials model via Proc Logistic (SAS ver. 9.4) was used to model the effect of survival to day 14. In addition, Proc Phreg (SAS ver 9.4) was used to model survival functions over the duration of the study. In both cases non-significant interactions and variables were removed until only significant factors were left in the model.p-values presented are averages of the p-values from the 10 runs. Because post-hoc tests are limited in model II ANOVAs, a set of tests were run comparing Bt1 (A+W−) to Bt3 (A+W+), Bt1 (A+W−) to Bt1 (A−W−), and Bt3 (A+W+) to each of its antibiotic-treated sub-strains Bt3 (A−W+) and Bt3 (A−W−). Comparisons were also made between the susceptible W82 genotype and each of the resistant genotypes  using only data from Bt1 (A+W−) and Bt3 (A+W+). As with the overall analyses, 10 runs each, using seven random samples were used for the post-hoc tests and the p-value was averaged over all runs. The alpha used in these analyses was Bonferroni adjusted to account for multiple tests.We used a model II ANOVA to determine whether soybean genotype had an impact on the variability of the total number of nymphs produced . Only aphids that survived to the end of the experiment were included in the analysis. The analysis does not allow for unequal sample size, thus a random sample of seven samples from each aphid strain/genotype combination was used in the analysis. The analysis was repeated 10 times, with a different selection of samples, to ensure that results were consistent. The p value for each main effect and interaction for the 1,000 model experiments.We ran the same ANOVA model used for the whole plant experiments for successive sets of three models representing the three replicates used in the greenhouse and growth chamber experiments (each run was randomly selected so successive model selection is still random). We calculated the average 2X = 22.53, p = 0.0320) and environmental chamber experiments , demonstrating aphid biotype specificity toward plant host genotypes. In both experiments, all strains of Bt3 had significantly higher aphid counts than the two strains of Bt1  and (A−W−), behaved similarly, with reduced fecundity on all resistant genotypes, the three Bt3 strains, (A+W+), (A−W−), (A−W+), also behaved similarly to each other with higher fecundity on W82 and Rag2 and lower fecundity on Rag1 and Rag1 + 2 (Rag1 (letter f), Rag 2 (letter a), or Rag 1 + 2 (letter d); likewise, there was no significant difference for Bt1 strains on Rag1, Rag1 + 2 (letter g), or Rag 2  (A−W+) had significantly lower aphid counts on W82 plants  . In the 2 plants , and all numbers . There wAverage number and cumulative number of nymphs deposited per day per genotype leaf over the duration of the experiment  were cha2X = 10.32, p = 0.5883); however, both main effects were significant . In Tukey–Kramer post hoc tests fecundity on all four soybean genotypes were significantly different from each other (W82 plants had the highest number of aphid nymphs deposited (63 ± 0.94 nymphs deposited), while the Rag1 plants had the lowest number of nymphs deposited over the same time-period (37 ± 2.32 nymphs deposited). The two Bt1 strains had significantly lower fecundity than the three Bt3 strains. The pattern of overall fecundity, with the susceptible genotype (W82) having the highest and Rag1 the lowest number, was consistent across all aphid strains (A+W−) and Bt1 (A−W−) deposited significantly fewer nymphs on all genotypes compared to the three Bt3 strains on the same genotypes  and was therefore removed, prior to running the main effects model. Both soybean genotype  and aphid strain  were significant. A total of eight contrasts were tested in this experiment resulting in a Bonferroni adjusted alpha level of 0.0063. Aphids grown on the susceptible genotype (W82) had a higher maximum number of nymphs deposited than those cultured on any resistant genotype  (A+W−) and Bt1 (A−W−), but Bt1 (A+W−) had significantly lower maximum number of nymphs deposited than Bt3 (A+W+) (A+W+) had significantly higher maximum number of nymphs deposited than either Bt3 (A−W+) or Bt3 (A−W−), but Bt3 (A−W+) and Bt3 (A−W−) were not significantly different from each other. Arsenophonus significantly impacted Bt3 but not Bt1.For the maximum number of nymphs deposited, the interaction term of soybean genotype by aphid strain was not significant  . There w3 (A+W+) . Bt3 . When on the W82 plant genotype all aphid strains deposited nymphs earlier than on resistant genotypes (A+W−) strain deposited nymphs later than the other strains on all plant genotypes except for Rag1 + 2 genotype where Bt1(A−W−) was the most delayed. Arsenophonus impacted the day of first nymph deposition in Bt1 but not Bt3.There was a significant difference in the day that the first nymph was deposited  was the first strain to deposit the maximum number of nymphs in a day, while Bt1(A−W−) was the last, but there was not a significant difference within the Bt1 or Bt3 strains . Neither soybean genotype nor the interaction was significant . Arsenophonus and Wolbachia did not impact the day maximum number of nymphs were deposited.Bt3. Aphid strains had significantly lower survival rates on Rag1 and Rag1 + 2 compared to the susceptible genotype (W82). Survival rates and functions of aphids for all strains on Rag2 were not significantly different from aphids on the W82 genotype  but not among aphid strains . The analysis only included aphids that survived the entire experiment , providing a conservative measure of variability. Aphids cultured on the susceptible genotype (W82) had less variability in the total number of nymphs deposited when compared to the resistant Rag soybean genotypes . The Rag1 and Rag 1 + 2 genotypes had a stronger impact than Rag2, and Bt1 strains are more strongly impacted by Rag1, Rag1 + 2 and Rag2 than are Bt3 strains. For the Bt1 strain, which does not have Wolbachia, Arsenophonus decreased fecundity on Rag1  and increased total fecundity on Rag1 + 2   . ArsenopWolbachia nor Arsenophonus impacted fecundity on Rag1, however, Arsenophonus decreased fecundity on Rag1 + 2 (87% of models significant at p < 0.05 (A−W+) vs. (A+W+), diff = 2,312–1,798 = 514) (Wolbachia and Arsenophonus were present . For the Bt3 strain when on W82, the presence of Wolbachia  resulted in higher fecundity than when Wolbachia was absent .For the Bt3 strain, neither 8 = 514) . FecundiA. glycines with or without Arsenophonus or Wolbachia, in experiments using whole plants or individual aphids, but did observe differences in patterns of fecundity of individual aphids on detached leaves. We also observed differences in overall aphid fecundity when individual aphid data was used in simulations of whole plant experiments  in the individual based model results. In addition, Wolbachia increased overall fecundity in the individual based models when Bt3 was reared on W82. In the Bt1 strains, the presence of Arsenophonus only impacted the first day of deposition, which was delayed when aphids were placed on Rag1, Rag2, and W82. When Bt1 aphids without Arsenophonus were reared on Rag1 + 2, first day of deposition was slightly delayed compared to the Bt1 strain with Arsenophonus  and is further confirmation that Rag2 impacts Bt1 more than Bt3  and host health and resistance. As was seen in the greenhouse experiments, plant health can be compromised in whole plant experiments as aphid populations increase. Poor host health then reduced aphid populations. This occurs on the hosts most likely to have rapid aphid growth thereby confounding plant health effects with aphid population growth. Individual based models remove this impact by removing intraspecific competition and removing host health effects by maintaining healthy leaves resulting in a closer approximation to the aphid intrinsic rate of increase. Combining individual data with individual based models of population growth therefore isolates the impact of fecundity and host resistance from intraspecific competition and host health. Moreover, the finer observations obtained through individual aphid studies on detached leaves provides fecundity curves necessary for individual based modeling of the evolution of virulence .Previous work has used patterns of fecundity, such as those obtained through this research, in individual based modeling  to deterW82 but higher variability on resistant genotypes  exposure to high and low temperatures, (2) poor plant nutrient status such as low iron, (3) toxin exposure including pesticides, and (4) plant chemistry of the overwintering hosts Rhamnus cathartica, R. alnifolia and possibly Frangula alnus (syn. Rhamnus frangula) (Moreover the effect of rangula) , 2005.The original contributions presented in the study are included in the article/The manuscript presents research on animals that do not require ethical approval for their study.RG, EPW, CBH, and GLH conceived the study. RG, EPW, CBH, RM, AF, AL, AKD, TKH, FNS-A, and MQN carried out the work of setting up experiments, microinjections, testing of aphids infection status, and collected the data. RG, EPW, and CBH analyzed data. EPW wrote the model. RG, CBH, and GLH secured funding. RG and EPW wrote the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by generous grants from the U.S. Mid-West farmers through the checkoff program funds from the United Soybean Board (USB), Illinois Soybean Association (ISA), and the North Central Soybean Research Program (NCSRP) to RG and GLH. Funding was also provided by USDA, Agricultural Research Service, Integrated Management of Soybean Pathogens and Pests, Accession number 432114 to GLH.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


At the same time, increased atmospheric CO2 could enhance weathering of carbonate rocks  and increase the total concentration of dissolved carbonate species. Both processes enhance phototransformation by the carbonate radical (CO3•−), as shown for the non-steroidal anti-inflammatory drug paracetamol, provided that the dissolved organic carbon of water does not undergo important fluctuations. Climate change could also affect hydrology, and prolonged drought periods might considerably decrease flow rates in rivers. This is a substantial problem because wastewater pollutants become less diluted and, as a result, can exert more harmful effects due to increased concentrations. At the same time, in low-flow conditions, water is also shallower and its flow velocity is decreased. Photochemical reactions become faster because shallow water is efficiently illuminated by sunlight, and they also have more time to occur because water takes longer to cover the same river stretch. As a result, photodegradation of contaminants is enhanced, which offsets lower dilution but only at a sufficient distance from the wastewater outlet; this is because photoreactions need time (which translates into space for a flowing river) to attenuate pollution.In temperate environments, climate change could affect water pH by inducing enhanced dissolution of CaSO The contaminants of emerging concern (CECs) are a significant threat to surface water bodies because of their adverse effects, even at low concentration values, on aquatic life forms and potentially on human health as well, if the water is used for irrigation purposes, recreational activities, or as a source of drinking water ,2. CECs An important class of CECs is represented by pharmaceuticals and personal care products, of which non-steroidal anti-inflammatory drugs are among the medicines most used by the general population and are often found in surface waters ,5. The b•OH) and carbonate (CO3•−) radicals and singlet oxygen (1O2), as well as the excited triplet states of CDOM (3CDOM*) acetic acid, DIC), and naproxen ((2S)-2-(6-Methoxy-2-naphthyl)propanoic acid, NAP). These compounds were chosen because they all undergo fast photodegradation. Furthermore, they follow different photoreaction pathways because DIC and NAP are mainly degraded by direct photolysis , while Aeactions  21]. Fu. Fu3•− rThe main novelty of this work is the consideration of the photochemical impact of changes that will increasingly affect surface-water environments in the near future.Ctot = [H2CO3] + [HCO3−] + [CO32−], have the potential to primarily affect processes induced by CO3•−, which is produced by oxidation of inorganic carbon species  and [CO3•−] as a function of pH, and then to predict the photodegradation kinetics of APAP. This compound was chosen because reaction with CO3•− accounts for over 50% of APAP photodegradation, at least for DOC < 4 mgC L−1 , which are reasonable values for surface freshwaters  = 10−pH, Ka1 = 5 × 10−7, and Ka2 = 5 × 10−11.The concentration values of the three carbonate species as a function of pH and  follows :(5)[H2CO•OH] and [CO3•−] calculated by APEX, as a function of pH in the above conditions. kAPAP.3•−] increases with increasing pH. Below pH 7, the reason is the lack of significant reactivity between H2CO3 and •OH, combined with the ability of •OH to oxidise HCO3− to CO3•− (reaction 1) . The almost constant [CO3•−] at pH 7–8 is accounted for by the fact that bicarbonate is the main inorganic carbon species reacting with •OH in such pH interval to produce CO3•−, while increasing [CO3•−] above pH 8 is due to the increasing role of carbonate in CO3•− production.As can be seen in ction 1) . As a co•OH of carbonate compared to bicarbonate   is almost constant at pH 7–8 and decreases at pH > 8   and of oction 8) ,24. Scavt pH > 8 a.CO32− 3•−] and [•OH] were investigated in the case of APAP photodegradation. As shown in 3•−, while •OH plays minor role. Furthermore, 3CDOM* and the direct photolysis of APAP are not much affected by pH [The implications of the previously mentioned changes in [COed by pH .kAPAP (3•−] (kAPAP increases by ~5 times as pH increases from 6.5 to 9 at constant Ctot (note that photodegradation of DIC or NAP would be poorly affected in the same conditions).As a consequence, the pH trend of kAPAP b mirrorsAP , which measures the content of dissolved organic matter (DOM) in water. Actually, DOM is able both to scavenge CO3•− directly and to consume •OH, thereby additionally inhibiting the formation of CO3•− by reactions 1 and 2. Therefore, [CO3•−] is considerably inhibited by a DOC increase  decreases with increasing pH, as shown in •OH] decrease, the pseudo-first-order rate constant kBP4 fluctuates around 0.15 day−1 in the pH interval 6.0–9.5 , which has pKa~7.5 and mainly reacts with otolysis . As showotolysis . Figure 3•− is also affected by total inorganic carbon levels. An increase in the concentration of total inorganic carbon in surface waters can be the consequence of increasing levels of atmospheric CO2. This phenomenon enhances the weathering of rocks such as dolomite ((CaMg)(CO3)2), with dissolution of magnesium in the form of bicarbonate (reaction 9)  would increase because of enhanced formation of CO3•− in reactions 1, 2, and 8. At the same time, [•OH] would decrease because of enhanced scavenging in reactions 1 and 2 (Reaction 9 accounts for the observed increasing levels of Mger water . If Ctot 1 and 2 a.3•−] (Ctot (increasing DOC would offset an increase in Ctot and vice versa).In a similar way, as in the case of pH, APAP photodegradation would follow the trend of [CO3•−] b but DOC3•−] and kAPAP can be enhanced significantly by increasing pH and increasing Ctot, provided that the DOC does not undergo a parallel increase that might be able to offset such variations. Actually, given the observed long-term changes of pH, inorganic carbon, and DOC in surface freshwaters , the molar concentration values of NO3−, NO2−, HCO3−, and CO32−, and the DOC [mgC L−1]  ,31,32,33•OH], [CO3•−], [1O2], and [3CDOM*], as well as the pseudo-first-order photodegradation rate constant kP . Such output data are here relevant to mid-latitude spring equinox conditions.For a given pollutant P, based on the above input data, APEX returns the steady-state concentrations [3−] and [CO32−], which can be obtained on the basis of pH and Ctot by means of Equations (6) and (7). Such equations thus played a key role in modelling the photochemical implications of pH and total carbonates. In the case of river flow changes, APEX can take into account the variations of d, i.e., the acceleration of photochemical reactions in shallower water columns. In contrast, calculations involving U and l had to be carried out as a posteriori on APEX output data.The pH value affects the concentrations  and decrease [•OH], thereby affecting the phototransformation of compounds that react with the two PPRIs. For instance, APAP photodegradation would be enhanced because this compound is mainly transformed upon reaction with CO3•−. However, for these effects to be highlighted, it is very important that changes in pH and Ctot take place at almost constant DOC, because relatively small DOC variations could easily offset/overcome important modifications of pH and Ctot. Furthermore, compounds undergoing acid⇆base equilibria would be affected in different ways depending on the relative photoreactivity of their acidic and basic forms, as shown above for BP4 and TRIC.Possible impacts of climate change on the chemistry and hydrology of surface waters in temperate environments have the potential to affect photodegradation processes and the photoinduced attenuation of contaminants. An increase either in pH ).1O2 and a corresponding enhancement of reactions induced by 3CDOM*.A final issue is that lake and river waters might become increasingly oxygen-poor as a likely consequence of climate change ,35. The


Infrared machine vision system for object perception and recognition is becoming increasingly important in the Internet of Things era. However, the current system suffers from bulkiness and inefficiency as compared to the human retina with the intelligent and compact neural architecture. Here, we present a retina-inspired mid-infrared (MIR) optoelectronic device based on a two-dimensional (2D) heterostructure for simultaneous data perception and encoding. A single device can perceive the illumination intensity of a MIR stimulus signal, while encoding the intensity into a spike train based on a rate encoding algorithm for subsequent neuromorphic computing with the assistance of an all-optical excitation mechanism, a stochastic near-infrared (NIR) sampling terminal. The device features wide dynamic working range, high encoding precision, and flexible adaption ability to the MIR intensity. Moreover, an inference accuracy more than 96% to MIR MNIST data set encoded by the device is achieved using a trained spiking neural network (SNN). Designing an infrared machine vision system that can efficiently perceive, convert, and process a massive amount of data remains a challenge. Here, the authors present a retina-inspired 2D optoelectronic device based on van der Waals heterostructure that can perform the data perception and spike-encoding simultaneously for night vision, sensing, spectroscopy, and free-space communications. The current IR machine vision systems usually rely on physically separated IR imaging devices and von-Neumann computing architectures to perform the real-time information perception and processing, respectively2. This system generates large amounts of redundant data being exchanged between sensory terminals and processing units, resulting in high data latency, large computing load and low energy efficiency5. The lack of compactness and computing efficiency is rapidly making the existing system obsolete in the era of big data and the internet of things.Infrared (IR) machine vision that can efficiently perceive, convert, and process the massive amount of IR optical information of the observed objects has become an important technology for various scenarios requiring crucial decisions, which include autonomous driving, intelligent night vision, military defense and medical diagnosis7. These encoded spike trains are subsequently transmitted to the visual cortex of the brain for information processing9. The discretization and stochasticity of spike-encoded information allow long-distance communication and efficient neural computation8. Following the infrastructure and operation mechanism of human visual sensory system, it is highly desired to have the perception and encoding of external optical stimuli integrated in one neuromorphic device for realizing a compact, efficient, and intelligent IR machine vision system.In contrast to the inefficient machine vision system, the human visual sensory system consists of a very compact retina that can perceive, encode and process a huge visual data set by harnessing distributed and parallel neural networks. In the real world, continuous light stimuli are first received by the sensory neurons in the human retina and then encoded as discrete spike trains generated via a set of neural algorithms11. Recently, notable progress has been made with 2D vdWs heterostructures in developing neuromorphic sensors, encoders and processors18, presenting a development trend towards all-in-one devices with functionalities integration20. However, these studies focus only on the visible and near-infrared (NIR) spectral ranges, while such integrated neuromorphic devices operating in the MIR range would greatly advance IR machine vision systems for autonomous driving, intelligent night visions, defense, and medical applications, and improve the versatility of neuromorphic systems. In addition, the demonstrations of encoding functionality in previous studies are limited in electronic approaches with electrical bias22. An integrated MIR neuromorphic device with the perception and encoding functionalities driven by an all-optical approach is expected to shed light on the technological development of high-speed and zero-bias information coding of IR machine vision.2D van der Waals (vdWs) heterostructures become the promising candidates for achieving such a goal due to their superior optical functionalities such as strong light-matter interaction, tunable bandgap and the potential compatibility with CMOS platform2 is designed such that it can perceive external light in the MIR spectral range (at ∼4.6 μm) while simultaneously encode the received MIR information into spike trains by harnessing a stochastic NIR sampling terminal (at ∼730 nm excitation). Featuring high MIR detectivity (9.6 × 108 cm Hz0.5/W) and fast NIR photoresponse rate (∼600 ns), the device successfully demonstrates a typical neural encoding algorithm of rate-based encoding with wide dynamic working range and high encoding precision for MIR illumination intensities. Our device demonstrates the adaption ability to intensity variation of MIR signal, which is analog to the human eye’s visual adaption to the change in ambient light intensity in the visible range. Furthermore, a trained SNN achieves an inference accuracy of more than 96% to the MIR MNIST data set which is encoded into spikes by the device. The retina-inspired 2D MIR optoelectronic device integrating perception and encoding functionalities has the potential to perform MIR machine vision in a highly compact and efficient way.In this work, we report an all-optical driving 2D MIR optoelectronic retina with simultaneous perception and encoding functionalities without inducing electrical bias. The neuromorphic 2D vdWs heterostructure composed of b-AsP and MoTe22. Figure 8. Notably, the encoding process exhibits the inherent stochasticity which is involved in the spike generation and enhances the noise tolerance of spikes. Inspired by the human visual system, in this work, a 2D optoelectronic retina capable of simultaneously perceiving and encoding MIR optical stimuli is proposed and demonstrated by using a 2D b-AsP/MoTe2 vdWs heterostructure. Upon the stimulation of MIR signals, the photo-excited current (IDS) of the 2D optoelectronic device is measured from source/drain electrodes at zero bias, which mimics the optical signal collection and conversion of the photoreceptors in the human retina. Meanwhile, programmable NIR optical pulses with stochastic intensity cause corresponding fluctuation of IDS, where a spike is generated when the IDS exceeds the threshold line (ITC), emulating the encoding scheme of ganglion cells. The as-generated spike trains with coded MIR information are finally processed by a trained SNN for intelligent tasks, such as classification and decision17.The visual system is one of the important sensory organs for humans to perceive the external world as more than 80% of the environment information is captured in human eyes24, and MoTe2 with an appropriate bandgap of ∼1.0 eV serves as the NIR sensitizer26. Both b-AsP and MoTe2 exhibit high hole mobility of ∼145 and ∼15 cm2/Vs 28 is lower than that of Au (∼200 W/mK)29. Such lattice temperature distribution promotes the diffusion of holes in the b-AsP from the MoTe2 contact side to Au electrode contact side, thus forming a positive PTE photocurrent under zero bias with b-AsP as the source terminal. Under NIR laser global illumination, both b-AsP and MoTe2 layers generate electron-hole pairs which are separated by the built-in electrical field with direction pointing from b-AsP to MoTe2 side at the junction. The photo-generated electrons and holes move toward b-AsP and MoTe2, respectively, which contributes to the negative photovoltaic photocurrent. As shown in Fig. 32. Moreover, the detectivity of the device to MIR illumination can reach up to ∼9.6 × 108 cm Hz0.5/W. More details and discussions on the photoresponse performance under MIR and NIR illumination are provided in Supplementary Figs. In the proposed 2D MIR optoelectronic retina, the b-AsP is used as the MIR photosensitive layer owing to its narrow bandgap of ∼0.15 eV and high MIR optical absorption efficiency of ∼10%IDS) can be observed when the device is simultaneously illuminated by MIR with a certain power density and NIR with various power densities. The photoresponse under the simultaneous illuminations shows high repeatability and stability, evidenced by multiple and reproducible switching  once the ITC and NIR power density (PNIR) distribution are given. More important, the stable photoresponse can be still maintained under NIR illumination with a frequency of 100 kHz  of 10 μs (on/off = 5/5 μs), which is analogous to inherent stochasticity8. This sampling period is determined by taking into account the NIR response rate. Figure u = 130 mW/cm2, and standard deviation, σ = 75 mW/cm2 for spike rate-based encoding  and therefore results in a train of IDS with 100 sampling points.  As-recorded IDS trains with ITC = 0 nA and corresponding spike trains are shown in Fig. ITC is discussed in Supplementary Fig. IDS value higher than ITC = 0 nA stimulates one spike. Average spike rate for each PMIR is calculated according to the generated spike train (spike rate = n is the number of spikes in the output spike train), as shown in Fig. PMIR within ∼80.21 W/cm2. The error in spike rate is about 0.9% due to the fluctuation of IDS waveform. Notably, a fast response speed to NIR light for our device is helpful to increase time-steps over a fixed encoding time which equals the multiplication of time-steps and TS. Insufficient time-steps for one MIR intensity cannot guarantee high encoding accuracy  and presented in Fig. PMIR. To encode the PMIR distribution of targets into corresponding spike trains, another NIR light whose PNIR is sampled from a Gaussian distribution with u and σ of 130 and 75 mW/cm2 is also incident into the device at the same time. The recognized image after rate encoding by our device is shown in Fig. PMIR resolution.To demonstrate the adaption ability of our MIR optoelectronic retina, we establish a testing setup shown in Fig. u and σ for sampling the PNIR can conveniently tune the dynamic working range. The increase of σ extends the dynamic working range, while the increase of u shifts the dynamic working range to a high PMIR range. The experimental and simulation results are presented in Fig. u, σ). For example, when the  changes from  to , the dynamic working range shifts to the high PMIR range, which results in the correct encoding of the high-power target (ix) with CC improving from 83% to 98% but failed encoding of the low-power target (ii) with CC = 0. When the σ is increased from 35 to 75 at u = 130, the CCs of targets (ii) and (ix) both reach 98% without any encoding failure, which verifies the function of σ used to extend dynamic working range. The u should keep a high power when σ is relatively high (like σ = 75 here). Otherwise, the background noise of the encoded image will be magnified due to the no-zero spike rate at PMIR = 0, such as the results at  = , causing an extra interference for identifying targets. To magnify the details of PMIR distribution inside one certain target, a high encoding precision is required and can be achieved by decreasing σ under a suitable u. For example, the target (ii) at  =  has a higher contrast than the case at  = . Therefore, optimizing the u and σ values is critical in achieving a suitable dynamic working range and high encoding precision and help exhibit the eye’s visual adaption ability to different MIR targets in our device.The adjustment of 38, SNN is believed to be a more efficient neural network that rarely requires high-precise multiplication. Also, the density of binary spikes required for SNN is much sparser than that for ANN, mitigating the storage memory and energy requirements38. The energy-delay product of SNN running on a spike-based neuromorphic hardware has been proved by four-orders magnitude lower than that of the traditional DNN running on a CPU over one batch size39. We use the snnTorch platform introduced by Eshraghian38 to establish a fully-connected three-layers SNN that consists of the input layer, hidden layer and output layer with 784, 200 and 10 neurons, respectively, as shown in Fig. 40, as shown in Fig. Xi(t) of the ith spiking neuron are modulated by synaptic weights Wi to produce a resultant current Vmem of the post-neuron in the next neuron layer, given as:β and k are membrane potential decay rate and the number of neurons in this layer, respectively. The T is the transposition operation. The Vmem of the post-neuron will integrates incoming spikes until it reaches membrane threshold VTH where the Vmem is reset to zero. Meanwhile, the post-neuron generates an output spike which acts as the input spike of next neuron layer. In our device, ITC is equivalent to the VTH.Lastly, we utilize the device to encode the MIR MNIST data set into spike trains, which enables the successful realization of SNN-based digit classification tasks with inference accuracy of more than 96% (see the “Methods” section for the details about preparing the MIR MNIST data set). Compared to traditional artificial neural network (ANN)u and σ values of Gaussian distribution for sampling NIR light control the dynamic working range and encoding precision. If the dynamic working range mismatches the PMIR range of the target within  or the encoding precision is insufficient, the inaccurate translation of the target by encoded spikes will cause inference error of SNN. Figure Pmax of MIR MNIST test set varies from 0 to 80.21 W/cm2 at different values of u and σ. A relatively low σ of 35 makes the dynamic working range too narrow to encode the digits with Pmax lower than 10 W/cm2, resulting in 9.8% classification accuracy. When σ increases to 55, the enlarged dynamic working range can cover both low and high Pmax and allows the classification accuracy to become higher than 96%. However, the further increase of σ to 75 decreases the encoding precision. The spike rate resolution is not sufficient to support accurate classification for the low-Pmax case. Additionally, the background noise is a little magnified, hampering the inference of SNN. The u value controls the position of the dynamic working range, and it therefore controls the position of high-accuracy working range of SNN. For example, the working range with classification accuracy higher than 96% gradually moves to higher PMIR range when u increases from 100 to 130 with σ = 15, shown in Fig. u, σ) =  to encode the target with Pmax of 21 W/cm2. The performance of our device is already comparable to an ideal linear encoder. However, insufficient time-steps result in inadequate representation of targets, and therefore significantly decline the classification accuracy. The encoded images of target “3” using time-steps of 1, 5 and 100 are given in Fig. Pmax are also provided in Supplementary Fig. The classification performance of SNN significantly depends on the dynamic working range and encoding precision of the device. As mentioned in Fig. 2 vdWs heterostructure. The device features a high MIR (∼4.6 μm) detectivity of 9.6 × 108 cm Hz0.5/W and a fast NIR (730 nm) response rate of ∼600 ns without inducing electrical bias. Impressively, the proposed device could not only perceive the MIR illumination stimuli, but also encode it into rate-based spike trains with the assistance of a stochastic NIR sampling terminal. Moreover, device’s encoding range and precision can be flexibly adjusted for different MIR illumination intensities. The device encodes the MIR MNIST data set into spike trains which enables SNN to achieve digit classification with an accuracy higher than 96%. Our work provides a promising routine for constructing compact and efficient MIR neuromorphic devices for night machine vision, military, defense, and medical diagnosis. We anticipate that the optical approaches of realizing neuromorphic functions based on 2D vdWs heterostructures have the potential of wide bandwidth up to tens of gigahertz when combined with integrated guided-wave nanophotonics41, bringing in the advantages of low data latency and high energy efficiency.Inspired by the human vision system with the function of perceiving, transmitting and processing the external environment information, we demonstrate a compact retina-inspired MIR optoelectronic device using a 2D b-AsP/MoTe2 flakes are sensitive to the water and oxygen in the surrounding environment, a dry transfer method was applied to fabricate the 2D b-AsP/MoTe2 vdWs heterostructure. The contact electrodes (5/50 nm Cr/Au) were first patterned on a SiO2/Si substrate by standard photolithography and electron beam evaporation. The exfoliated 2D b-AsP and MoTe2 flakes from bulk crystals were then dry transferred onto the electrodes. Finally, h-BN encapsulation was used to protect the device from degradation. The morphology and thickness of as-fabricated device were characterized by optical microscope (Nikon), atomic force microscope (Bruker Dimension Icon). Scanning photocurrent mapping was performed by using confocal micro-Raman spectroscopy  equipped with a focused 532 nm laser.Because 2D b-AsP and MoTe2 vdWs heterostructure. For the encoding measurements, the device is simultaneously illuminated by 4.6 μm MIR laser with a fixed power density and pulsed 730 nm laser with Gaussian distribution power densities. The sampling period (TS) of 730 nm laser is set to 10 μs and its amplitude is determined by the desired encoding algorithm. The fast current sampling was collected by means of an oscilloscope .The measurements of electrical and photoelectric properties were performed at room temperature and under ambient air conditions. A digital source meter  was used to apply voltage to the device and record the generated current. A MIR quantum cascade laser (QCL)  with tunable wavelength from 3.5 to 11.0 μm was employed as the external stimuli. The power of MIR laser was recorded by a thermal power meter . A power adjustable 730 nm laser  was applied as the stochastic terminal and its power density was measured using a power meter . The laser spots of MIR laser and 730 nm laser are about 100 μm, which is larger than the size scaling of the as-fabricated 2D b-AsP/MoTePMIR distribution image of figure “3” targets in mask, the responding photocurrent of device to every pixel of mask is collected by oscilloscope. The mask has 300 × 300 pixels in which each “3” target occupies 100 × 100 pixels. The PMIR of 4.6 μm laser from QCL on every “3” region (100 × 100 pixels) is different. When the mask is scanned by pixels, the responding photocurrent of each pixel depends on the optical flux of 4.6 μm laser passing through this pixel region. According to the mapping relation of photocurrent and PMIR given in Supplementary Fig. PMIR for every pixel can be estimated from the photocurrent obtained by experiment, and finally constitutes the PMIR distribution image shown in Fig. To recognize the Pmax]. Once the Pmax is set, every image in the prepared MIR MINST data set with a size of 28 × 28 pixels is first flatten to obtain 784 analog optical power density of MIR laser. The MIR laser with a certain optical power density can be detected and encoded by our device into spike trains as the input of SNN.The MIR MNIST data set is obtained by mapping pixel values of traditional MNIST data set ranging in  to optical power density of 4.6 μm laser ranging in [0, 38 in order to avoid dead neuron problem. The loss function and optimizer used here are cross-entropy loss and Adam optimizer. There are 60,000 and 10,000 MIR MNIST images used for training and test, respectively. The number of hidden neurons and membrane potential decay rate are two super-parameters affecting classification ability of SNN. More hidden neurons and higher β can enhance the classification accuracy (seen in Supplementary Fig. β of real synaptic devices hardly reaches 100%, and therefore the β in our work is set to 0.95. The number of hidden neurons is set to 200 considering the trade-off between performance and complexity. After training around 450 iterations in one epoch with the batch size of 128, the loss of train and test sets all converge to a steady level, verifying SNN is well trained without under-fitting and over-fitting problems (seen in Supplementary Fig. For training of SNN, a surrogate gradient descent algorithm is used to update synaptic weightsSupplementary informationPeer Review FileSource data