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Reaction Kinetics, Mechanisms andCatalysis ISSN 1878-5190 Reac Kinet Mech CatDOI 10.1007/s11144-013-0627-5

Kinetic study of competitive catalytictransfer hydrogenation on a multi-functional molecule: 4-benzyloxy-4′-chlorochalcone

Tyler Nguyen, Julia Arciero, JoshuaPiltz, K. Danielle Hartley, TimothyRickard & Ryan Denton

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Kinetic study of competitive catalytic transferhydrogenation on a multi-functional molecule:4-benzyloxy-40-chlorochalcone

Tyler Nguyen • Julia Arciero • Joshua Piltz •

K. Danielle Hartley • Timothy Rickard • Ryan Denton

Received: 22 May 2013 / Accepted: 26 August 2013

� Akademiai Kiado, Budapest, Hungary 2013

Abstract A combined chemical and mathematical approach was used to study the

reduction process of 4-benzyloxy-40-chlorochalcone involving catalytic transfer

hydrogenation with ammonium formate and palladium on carbon. Several solvents were

investigated to evaluate the effect of solvent-type on competitive reduction rates. A

mathematical model based on Michaelis–Menten reaction kinetics was developed using

a least squares approximation method to estimate several model parameters according to

time-dependent reduction data. The conjugated alkene was found to reduce fastest in all

solvents except alcohols, which is likely related to the solvent’s ability to support par-

tially-charged intermediate species in the RDS of aryl chloride hydrogenolysis. Sub-

strate concentration and pH dependence of the reduction were also investigated to

confirm prior mechanistic findings. Hydrogen transfer from the formate to palladium

appears to be rate determining in multiple reactions. Also, catalyst poisoning by HCl may

affect aryl chloride reduction more significantly than reduction of other functionalities.

Keywords Palladium � Catalytic transfer hydrogenation � Reduction � Multi-

functional � Mathematical modeling

Introduction

Catalytic transfer hydrogenation (CTH) is a proven technique in synthetic organic

chemistry that provides significant advantages over traditional hydrogenation

T. Nguyen � J. Piltz � K. D. Hartley � T. Rickard � R. Denton (&)

Department of Chemistry and Chemical Biology, Indiana University-Purdue University

Indianapolis, 402 N Blackford Street, Indianapolis, IN 46202, USA

e-mail: [email protected]

T. Nguyen � J. Arciero

Department of Mathematical Sciences, Indiana University-Purdue University Indianapolis,

402 N Blackford Street, Indianapolis, IN 46202, USA

123

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DOI 10.1007/s11144-013-0627-5

Author's personal copy

methodologies, which often involve special equipment and safety concerns in the

handling of hydrogen gas. An extensive list of inorganic and organic CTH donors

has been employed effectively. Formate salts are perhaps the most desirable

hydrogen donors because of an almost irreversible hydride donation caused by the

formation of CO2 [1]. Some reports have suggested avoiding the use of ammonium

formate due to its propensity to decompose into hydrogen gas [2], which could

potentially allow the reaction to proceed through a non-CTH mechanism. However,

it is widely accepted that little or no hydrogen gas is produced in the presence of a

suitable substrate [3, 4]. Ammonium formate was employed in this study due to its

increased solubility in organic solvents, such as formic acid, compared to sodium

and potassium formate salts [5]. CTH can utilize a wide variety of heterogeneous

and homogeneous catalysts, including palladium, platinum, and nickel species.

Palladium on carbon (Pd/C) has been recommended as the most active heteroge-

neous catalyst when using formate salts as the hydrogen donor [4]. The CTH of

numerous functionalities has been reported, including aryl (or similar) halides,

alkenes, a,b-unsaturated ketones, nitro groups, cyano groups, benzyl protecting

groups, and various carbonyl functionalities. Several in-depth reviews over the past

four decades summarize these findings [6–14].

CTH is often highlighted as a valuable technique for the selective reduction of a

single functionality on a multi-functional molecule. The ability to tune the various

reaction properties (solvent, hydrogen donor, temperature and catalyst) allows

greater chemoselectivity than many traditional methods. Solvent choice is

especially crucial since heterogeneously catalyzed hydrogenations involving

complex multiple-phase reaction systems are strongly influenced by the solvent’s

properties [15]. However, these effects are very complex, and thus relatively few

systematic studies have been performed. Rajadhyaksha and Karwa [16] summarized

several links between the following four solvent properties and their mechanistic

outcomes in heterogeneous hydrogenations: (1) different solubility of hydrogen in

the reaction mixture, (2) competitive adsorption of the solvent molecules on the

active sites of the catalyst, (3) agglomeration of catalyst particles, and (4)

nonbonding interactions between reactant or product molecules with the solvent.

The nonbonding substrate-solvent interaction was found to be most critical to the

rate when using a variety of alcohol and hydrocarbon solvents. Specifically,

increased substrate-solvent interactions were found to decrease substrate adsorption

ability. A similar study [17] used the substrate activity coefficient from the

Universal Quasi-Chemical Functional Group Activity Coefficients (UNIFAC)

contribution method [18] to explain this solvation effect on the substrate. Fajt

et al. [15] found the basic character of the solvent and its Hildebrand cohesion

energy density to be the most important solvent traits influencing hydrogenation of a

carbonyl functionality. Yet another report by Aittamaa and co-workers [19]

concluded that the hydrogenation of toluene with a nickel catalyst in various

hydrocarbon solvents was primarily dependent on hydrogen solubility in the

reaction. To summarize, solvent effects on heterogeneous hydrogenation rates have

been shown to depend primarily on the solvent used, substrate structure, and catalyst

type [20].

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Competitive multi-functional CTH, as presented in this study, are even more

complex because the solvent’s effect on the adsorption of one functionality will be

unique from the adsorption of a different functionality [21]. Overall, prior reports

seem to suggest that solvent effects on heterogeneous hydrogenations are specific to

the system being investigated and cannot be overly generalized. Further data on

solvent interactions in complex CTH systems may aid in bolstering these findings.

Mathematical modeling is a tool that has been used to understand the dynamics

of complex chemical interactions [22, 23]. The models tend to be complicated and

highly nonlinear, leading to challenges in solution techniques and parameter

estimation [24]. To study these systems, the models are often reduced to include

only primary reactions. For example, Black and Leff [22] used the law of mass

action to simplify a multiple pathway process for prescription drug-based agonist

binding into a single key chemical equation. Similarly, Doktorov and Kipriyanov

[23] used a simple model to examine binary chemical encounters in liquid solution

before developing a larger model. Several notable models have been utilized to

investigate the kinetics of reduction processes and solvent effects on reduction. Fajt

et al. [15] compared the Abraham–Kamlet–Taft (AKT) and Koppel–Palm (KP)

models to describe the effects of multiple solvents on the hydrogenation of 6-ethyl-

1,2,3,4-tetrahydroanthracene-9,10-dione. Glasser [25] used the Langmuir–Hinshel-

wood model to describe solvent effects on alkene hydrogenation using Pd/C. The

above models were not utilized in the current study primarily due to the lack of

hydrogen depletion data from the CTH of ammonium formate. Drougard and

Dececroocq [26] developed a model to predict solvent effects on homogeneous

catalysis reactions. However, that model is only appropriate for structurally similar

solvents and thus is not useful for the current study. Models based on Michaelis–

Menten kinetics have been applied to reduction reactions employing transition metal

catalysts in recent literature [27, 28]. These models were also employed by

Blackmond [29] in constructing a series of graphical rate equations that enable

analysis of the reaction from a minimal number of experiments; similar models will

be used in the present study.

Herein, we implement a combined chemical and mathematical approach to

investigate the reduction process of 4-benzyloxy-40-chlorochalcone, using Pd/C and

ammonium formate. The initial investigation of this reduction process was inspired

by an elegant laboratory experiment by Mohrig et al. [30]. To date, few reduction

studies address even simple chalcone systems [31–37]. The multi-functional system

presented here exhibits five potential reduction sites: benzyl protected phenol, aryl

chloride, aromatic rings, conjugated alkene and carbonyl of an a,b-unsaturated

ketone. Mathematical modeling is employed to elucidate the two most competitive

reduction processes (formation of compound Y from alkene reduction and

compound U from aryl chloride reduction, Fig. 1) as well as their secondary

reductions to form compound Z. As expected from previous reports [31], carbonyl

reduction was not observed in our study and will not be discussed further. Similarly,

reduction of the aromatic systems was not observed. Hydrogenolysis of the benzyl

ether, though observed in this study, will not be significantly discussed since it is a

thoroughly investigated deprotection strategy in organic synthesis. Our data for

hydrogenolysis of the benzyl ether (not included) supported accepted solvent

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reactivity trends involving increased rates with polar protic solvents like alcohols

and decreased rates for polar aprotic and non-polar solvents [38]. Furthermore, the

reported experimental data was not normalized due to the omission of benzyl ether

hydrogenolysis. The effect of diverse solvent types on these two competitive

reductions was also investigated. The mathematical model used to elucidate these

reductions is based on the law of mass action and Michaelis–Menten kinetics and is

composed of a system of ten nonlinear ordinary differential equations that can be

used to predict time-dependent changes in the concentration of each reactant or

product. Model predictions are compared with experimental kinetic data for each

reduction product and a least squares regression is used to estimate key reaction

rates in the system. Using the model results, the potential dependence of the reaction

rates on solvent polarity is discussed.

Experimental

Kinetic investigation of 4-benzyloxy-40-chlorochalcone reduction

4-Benzyloxy-40-chlorochalcone [39, 40] (compound X, 0.14 mmol), 10 % Pd/C

(Acros Organics AC19503, 10 mg, surface area 800 m2/g, particle size distribution:

d10 = 4 lm, d50 = 21 lm, d90 = 90 lm), and ammonium formate (Acros Organics

AC40115, 1.1 mmol) were reacted at atmospheric pressure and constant temper-

ature (75 �C) in 4.5 mL of various reagent grade solvents (methanol, ethanol,

acetone, ethyl acetate, tetrahydrofuran, dichloromethane, toluene, and cyclohexane)

stirring at high speeds (approximately 1,000–1,200 rpm) to minimize external mass

Fig. 1 Partial reaction network for the reduction of 4-benzyloxy-40-chlorochalcone (compound X) withammonium formate and Pd/C (Bn = –CH2–C6H5)

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diffusion limitations. Internal diffusion limitations were not verified due to the

broad range of palladium particle size and use of several solvents. Aliquots from the

well mixed suspension (0.5 mL) at 0.5, 1, 2, 3, 5, 10, 20, and 30 min were passed

through Celite and flushed with 1 mL of additional reaction solvent. Samples were

subjected to analysis on an Agilent 5975C Series GC/MSD using an Agilent HP-

5MS column (190915-433, 30 m 9 0.25 mm 9 0.25 lm). The reaction progress

was monitored by changes in the relative percent peak area. Variables such as the

effect of substrate concentration (0.03 and 0.06 mM) and acidic/basic additives

(1 mL of 0.1 M HCl or NaOH in ethanol solvent, performed as above) were

independently tested.

Mathematical modeling and parameter estimation

Complex reaction networks are composed of a variety of elementary steps [41]; in

particular, CTH may include: (1) chemisorption of the substrate to the catalyst, (2)

chemisorption of the hydrogen donor, (3) hydrogen transfer or hydride donation to

the catalyst surface, (4) formation of the product, and (5) desorption of the product

[4]. In the reduction of a 4-benzyloxy-40-chlorochalcone, these processes occur

simultaneously and yield intermediate products before forming a common product

Z (Fig. 1). Elementary mechanistic steps 1–3 above can be combined into a

collective ‘‘mostly reversible’’ step and steps 4–5 can be combined into a collective

‘‘mostly irreversible’’ step to yield a simplified system (Fig. 2).

Using these simplifications and the assumption that hydrogen ions (H) are in

excess, a mathematical model was developed to investigate the two pathways that

yield compound Z: (1) the initial reduction of the alkene (compound Y, rates k1 and

k2) and secondary reduction of the aryl chloride (compound Z, rates k5 and k7), and

(2) the initial reduction of the aryl chloride (compound U, rates k3 and k4) and the

secondary reduction of the alkene (compound Z, rates k6 and k8). The rate of each

elementary step in this simplified model is given by ki, with i = 1, -1, 2, 3, -3, 4,

5, -5, 6, -6, 7, and 8. Using the law of mass action, a system of ten ordinary

differential equations (ODEs) is defined that describes the rate of change of each

compound:

Fig. 2 Simplified reaction model where compounds X, Y, U, and Z are as in Fig. 1, H representshydrogen (from HCO2NH4), P is palladium, and C1–4 are catalyst-substrate complexes

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dx

dt¼ �k1PHX þ k�1C1 � k3PHX þ k�3C2

dp

dt¼ �k1PHX þ k�1C1 þ k2C1 � k5YPH þ k�5C3 þ k7C3 � k3PHX þ k�3C2

þ k4C2 � k6UPH þ k�6C4 þ k8C3

dH

dt¼ �k1PHX þ k�1C1 þ k2C1 � k5YPH þ k�5C3 � k3PHX þ k�3C2 þ k4C2

� k6UPH þ k�6C4

dY

dt¼ k2C1 � k5YPH þ k�5C3

dU

dt¼ k4C2 � k6UPH þ k�6C4

dZ

dt¼ k7C3 þ k8C4

dC1

dt¼ �k2C1 þ k1XPH þ k�1C1

dC2

dt¼ �k4C2 þ k3XPH þ k�2C2

dC3

dt¼ �k7C3 þ k5YPH þ k�5C3

dC4

dt¼ �k8C4 þ k6UPH þ k�6C4

ð1Þ

Positive terms in the ODEs represent formation of a product and negative terms

represent the reverse process. As mentioned above, adsorption processes to the

metal (k1, k3, k5, k6) are treated as reversible, while catalytic surface reactions and

desorption (k2, k4, k7, k8) are treated as irreversible [27]. Since hydrogen ions are in

excess, we let dH/dt = 0. The amount of palladium is assumed to be conserved (dP/

dt = 0) at each step of the reaction pathway and thus the sum of the rate of change

of catalyst concentration with time is zero:

dP

dtþ dC1

dtþ dC2

dtþ dC3

dtþ dC4

dt¼ 0 ð2Þ

Compounds Ci (for i = 1, 2, 3, 4) are assumed to form very quickly and thus are set

to their equilibrium values, that is, dCi/dt = 0. The rates of product degradation in

reversible processes were assumed to be zero (i.e., k-1 = k-3 = k-5 = k-6 = 0).

Given these assumptions, the system reduces to five ODEs for X, Y, U, Z, and H with

8 parameters. The system of ODEs is solved numerically using Matlab [42], and the

rates ki are estimated using a least squares regression to the experimental kinetic

data obtained from 4-benzyloxy-40-chlorochalcone reduction as monitored by time

changes in the relative area percentage of compounds in the reaction mixture. The

rate constants are chosen as the values that minimize the difference between the

normalized model predicted values and experimentally measured values of com-

pounds X, Y, and U at the measured time points. Experimental and model predicted

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values were normalized by the standard deviation of data obtained from the multiple

trials that were run for each solvent. For example:

Xexp ¼Xexp

Xstd

and X ¼ Xmodel

Xstd

ð3Þ

Y and U are defined similarly. A constrained optimization is performed to minimize

the following quantity while requiring k1 \ k2 and k3 \ k4:

X8

k¼1

X� Xexp

� �2 þ Y� Yexp

� �2þ U� Uexp

� �2 ð4Þ

This constraint was chosen according to previous reports on alkene [43] and aryl

halide [1, 4] reduction. In particular, the process by which the substrate or hydrogen

adsorb to the metal surface has been suggested as the rate-determining step (RDS).

In the present system, k1 and k3 correspond to the RDS in forming products Y and U,

and thus the model was optimized to experimental data with the constraints that

k1 \ k2 and k3 \ k4 in order to yield chemically relevant results.

Results and discussion

Reduction kinetics and modeling results

The reduction process of 4-benzyloxy-40-chlorochalcone using Pd/C and ammonium

formate was conducted using eight representative polar protic, aprotic, and non-

polar solvents: methanol, ethanol, acetone, ethyl acetate, tetrahydrofuran, dichlo-

romethane, toluene, and cyclohexane. Fig. 3 shows the relative area percentages of

compounds X, Y, U, and Z over time for the aforementioned solvents. For clarity, the

data were divided into two groups: non-polar solvents (tetrahydrofuran, dichloro-

methane, toluene and cyclohexane), which are shown in the left column of Fig. 3

(panels A, C, E, and G), and polar solvents (methanol, ethanol, acetone, and ethyl

acetate), which are depicted in the right column of Fig. 3 (panels B, D, F, and H).

As stated earlier, benzyl ether hydrogenolysis data was not included and the

remaining reduction data was not normalized. It should be noted that the

concentration maxima observed for compound Z derives from further reduction

processes (e.g. benzyl ether hydrogenolysis) that were not modeled in this study.

Fig. 4 shows the model fits to the reduction data for a representative solvent.

Model fits for the remaining solvents follow similarly. The optimized reaction rates

for the RDS are provided in Table 1 for all modeled solvents. The initial slopes of

the raw data (Fig. 3) agree with the model-predicted reaction rates, indicating that

the model can be used to predict the reduction rates with good accuracy.

Dichloromethane and cyclohexane rates were not included since the collected

experimental reaction data did not show reaction completion (Fig. 3, left column).

As justified previously, k1 and k3 were constrained to be less than k2 and k4,

respectively. Given these constraints, the RDS for the formation of compound Z are

k5 and k6, from compounds Y and U, respectively.

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Fig. 3 Experimental data for relative area percentage of compounds X, Y, U, and Z over reaction time.Panels A, C, E and G in the left column represent non-polar solvents: tetrahydrofuran (THF),dichloromethane (DCM), toluene, and cyclohexane. Panels B, D, F, and H in the right column representpolar solvents: methanol, ethanol, ethyl acetate, and acetone

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The coefficient of determination (R2) values for compounds X, Y, and U were

calculated between 0.73–0.94 for the modeled solvents (Table 2). A similar study

by Bernas and Myllyoja [27] gave R2 values of 0.7–0.9. It should be reiterated that

Fig. 4 Model fits (solid curves) to experimental data (black dots) for compounds X, Y, and U in ethanol

Table 1 Modeled rates for the formation of Y, U, and Z (% concentration/min)

Compound (RDS) Methanol Ethanol Acetone Ethyl acetate Tetrahydrofuran Toluene

Y (k1) 4.6 14.6 13.3 33.3 20.8 14.9

U (k3) 16.1 22.3 5.8 11.3 2.0 0.9

Z (k5) 32.7 75.3 25.5 91.6 36.5 36.0

Z (k6) 135.3 141.8 114.3 351.6 26.3 36.3

Table 2 The coefficient determination (R2) values for X, Y, and U in all modeled solvents

Compound Methanol Ethanol Acetone Ethyl acetate Tetrahydrofuran Toluene

X 0.8713 0.8425 0.9425 0.9023 0.8984 0.8629

Y 0.8874 0.8582 0.8375 0.8348 0.8543 0.7749

U 0.8343 0.8905 0.8904 0.7266 0.8388 0.7653

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model accuracy is limited by the large number of parameters being fit to the data as

well as the limited amount of experimental data available.

Reaction and mechanistic implications

Due to previous mechanistic findings for RDS in similar aryl halide hydrogenolyses

[1] and hydrogenations of a,b-unsaturated ketones [43] the model was constrained

to k1 \ k2 and k3 \ k4 for the formation of compounds Y and U, respectively.

Somewhat expectedly, the secondary reductions showed the rate determining

process to coincide with the ‘‘mostly reversible’’ adsorption step (i.e. k5 and k6).

It was also apparent that secondary reduction rates were higher than primary

reduction rates (i.e. alkene reduction k6 [ k1, aryl chloride reduction k5 [ k3).

Though an argument can be made for increased reactivity of compound U toward

alkene reduction versus compound X, increased reactivity of compound Y toward

aryl chloride reduction versus compound X would not be completely predictable.

Therefore, it is very possible that this result is caused by a saturation effect of

hydrogen on the Pd/C catalyst surface. Presaturation of the catalyst is known to

increase aryl chloride hydrogenolysis rates [2]. In addition, Suzuki and Suzuki [44]

suggest that once significant amounts of hydrogen have transferred to palladium,

excess hydrogen then migrates to the carbon surface and is easily accessible for

reduction. This result may also confirm that hydrogen transfer from the bound

formate to palladium is the RDS in both reductions, as has been previously

suggested with aryl chloride reduction [4]. Solvents also seem to play a role in this

process, as secondary alkene reduction rate increases (k6 [ k1) are most notable in

polar solvents, while secondary aryl chloride reduction rate increases (k5 [ k3) are

most notable in non-polar solvents.

When comparing initial reductions in Table 1, formation of compound U (aryl

chloride hydrogenolysis, k3) is faster than formation of compound Y (alkene

reduction, k1) in alcohol solvents, which are known to increase aryl chloride

reduction rates [2]. It should also be noted that alcohols have been known to act as

hydrogen donors in CTH reactions [45, 46] which may explain why some reductions

are predominantly fastest in ethanol. Observing the higher reduction rate of an aryl

chloride is a somewhat unexpected result, though no similar systematic study

between these two functionalities is known to the authors. With all other solvents,

alkene reduction rate was higher, especially in non-polar solvents. On the contrary,

when comparing secondary reduction rates, alkene reduction rate (k6) is higher than

aryl chloride hydrogenolysis rate (k5) in polar solvents and in non-polar solvents.

The initial reduction of aryl chlorides, with an unsaturated Pd/C catalyst, may place

more importance on solvent interactions in the hydrogen transfer/CO2 decompo-

sition step. It is also possible that the formation of HCl as a byproduct of aryl

chloride reduction is irreversibly poisoning the Pd/C toward further aryl chloride

reduction, even in polar solvents, whereas alkene reduction is less affected by HCl

poisoning [4].

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Effect of solvent polarity on rate

Initial reactivity trends seemed to suggest that solvent polarity might play a

significant factor in the RDS, potentially elucidating the increased rate of formation

of compound U compared to compound Y in alcohol solvents. To identify possible

trends between solvent type and reduction, the model predicted reaction rates are

graphed as a function of the solvent polarity index [47] and dielectric constant [48]

in Fig. 5 (constant values in Table 3). Panels A and B compare initial reductions (k1

and k3) and panels C and D compare secondary reductions (k5 and k6).

Table 3 Polarity indexes [47] and dielectric constants [48] of the modeled solvents

Methanol Ethanol Acetone Ethyl acetate Tetrahydrofuran Toluene

Polarity index 6.6 5.2 5.1 4.3 4.2 2.3

Dielectric constant 32.6 24.3 20.7 6.02 7.4 2.44

Fig. 5 Modeled reaction rates (% concentration/minute) graphed as a function of polarity index anddielectric constant. A k1 and k3 versus polarity index. B k1 and k3 versus dielectric constant. C k5 and k6

versus polarity index. D k5 and k6 versus dielectric constant

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All reduction rates investigated were highest in either ethanol or ethyl acetate,

with acetone and methanol lagging behind. Acetone rates (as compared to ethyl

acetate) and methanol rates (as compared to ethanol) were notably low when

considering the similar polarity index values. The higher dielectric constant values

in those pairs resulted in lower rates, which may result from a solvent-substrate

interaction preventing catalyst binding, for instance. Reduction rates for non-polar

solvents were generally lower. It should be noted that ammonium formate solubility

may be a factor of some importance. Of particular interest is the rate of k3 as

compared to k1 at high dielectric constants, which was not as prominent when

graphed as a function of polarity index (see Fig. 5, panel B). It could be concluded

that a solvent’s ability to support partially-charged intermediate species in the RDS

of aryl chloride reduction (k3, likely hydrogen transfer to palladium from formate) is

crucial. This appears to be less important for k1, which opens the possibility for an

alternate hydrogen donation mechanism or other compounding solvent-substrate

interactions that would mitigate this hypothesis. By observing the lack of such a

trend in k5 and k6, it appears that accumulation of hydrogen on the catalyst surface

minimizes this effect in aryl chloride reduction.

Substrate concentration

Time course experiments with two concentrations (0.03 and 0.06 mM) of compound

X were performed using the procedure described above to elucidate potential

differences in relative rates of reduction. Acetone was chosen as the solvent because

modeled rates for the formation for Y and U were somewhat similar. The formation

of compounds Y, U, and Z were relatively consistent between substrate concen-

trations, which further supports that the starting material was not involved in the

RDS for either reduction process. However, it should be noted that hydrogenolysis

of the benzyl ether was observed only in small amounts at 0.03 mM of compound X,

yet was accumulated in significant amounts at 0.06 mM of compound X. Benzyl

ether hydrogenolysis was ultimately occurring at both concentrations as evidenced

by further reduced products, but no accumulation of the phenol product was

Fig. 6 Acidic and basic additives show significant effects on reaction rate (A Compound Y, B CompoundU)

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observed at 0.03 mM. This data suggests that hydrogenolysis of the benzyl ether

may involve the substrate in the rate determining process.

Acidic and basic additives

From trends noted above, k5 (aryl chloride hydrogenolysis) is comparatively slower

than k6 (alkene reduction) during the secondary reduction than the primary

reduction (k3 vs. k1). One of the potential explanations involves HCl as a known

poison to the Pd/C catalyst [4]. Therefore, the reductions were performed with

aqueous acidic and basic additives to investigate this possibility (Fig. 6). As

evidenced in panel A, the addition of increased levels of HCl does not initially affect

the reduction of compound Y compared to ethanol. The addition of NaOH

significantly affects reduction in panel a, but likely because of the competitive rate

increase of compound U with the addition of NaOH in panel B. This supports results

that HCl poisoning of Pd/C is reversible with an aqueous base [4]. The addition of

HCl in panel B produced little increased poisoning effect compared to HCl

produced from aryl chloride reduction. It should also be mentioned that the effect of

water concentration on CTH reduction of the a,b-unsaturated ketones [49] and aryl

chlorides [4] is of some importance, which was not specifically explored here. As

the poisoning effects of HCl in aryl chloride hydrogenolysis have been well

established, we only sought to confirm or refute its presence in our study.

Conclusions

The relative rates of reduction of CTH of 4-benzyloxy-40-chlorochalcone were

modeled for the two most competitive processes, where conjugated alkene reduction

was found to be faster than the aryl chloride reduction in all solvents except

alcohols. Initial trends indicated that higher reaction rates correlated with increased

solvent polarity, though generalization of solvent effects on CTH to a single solvent

trait was only possible in a few instances. Secondary reductions to form compound

Z were significantly faster than subsequent primary reductions, which supports the

accumulation of hydrogen on the catalytic surface, where hydrogen transfer from

the formate to palladium was the RDS. To that end, substrate adsorption was

confirmed not to be rate determining in either reduction reaction. Catalyst poisoning

by HCl affects aryl chloride reduction significantly more than conjugated alkene

reduction.

Acknowledgments We would like to acknowledge Jeremy Martin, Tumare Iqbal, leaders/participants

from the 2010 CWCS Teaching Guided-Inquiry Organic Chemistry Labs Workshop and C344 students

from 2011-2013 for their valuable contributions to this work.

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